Influence of cycloplegia on the axial length prediction models in a peadiatric cohort

Influence of cycloplegia on the axial length prediction models in a peadiatric cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influence of cycloplegia on the axial length prediction models in a peadiatric cohort Ivo Soares, António Baptista, Oscar Torrado, Pedro Serra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7974559/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Clinical Relevance Accurate axial length (AL) estimation is vital for monitoring myopia progression in children, especially in primary care where optical biometers are often unavailable. Prediction models adjusted for cycloplegic effects may offer a reliable alternative. Purpose To assess the effect of cycloplegia on the accuracy and repeatability of several AL prediction models in a paediatric cohort, and to identify which models maintain minimal bias under both cycloplegic and non-cycloplegic conditions. Methods Ninety-six children (mean age 12.5 ± 2.4 years-old) underwent repeated measurements pre- and post-cycloplegia of spherical equivalent (SE), anterior corneal curvature (Kmean), and AL using the Myopia Master. Seven published prediction models incorporating SE, Kmean, age, and sex were evaluated. Agreement, bias, limits of agreement (LoA), coefficient of repeatability (CR), intraclass correlation coefficient (ICC), and regression analyses were used to assess performance and repeatability. Results Cycloplegia induced a hyperopic shift (mean + 0.79 D), most pronounced in emmetropic and hyperopic eyes. Measured AL and all models showed improved repeatability post-cycloplegia (measured AL CR decreased from ~ 0.14 mm to ~ 0.09 mm; ICC > 0.99). Pre-cycloplegia, models overestimated AL (mean differences MD from − 0.87 to − 0.24 mm); these biases reduced post-cycloplegia (MD from − 0.56 to + 0.10 mm). Models by Morgan, Queirós, and Lingham had the smallest bias (< 0.10 mm) and narrowest LoA (< 0.84 mm). Variation in SE accounted for ~ 97–99% of change in predicted AL; Kmean contributed ≤ 1.2%. Conclusion Cycloplegic refraction significantly enhances both accuracy and repeatability of AL prediction models in children. Models by Morgan et al., Queirós et al., and Lingham et al. performed best. Predictive models may be a valuable substitute in settings without access to optical biometers, provided cycloplegic measurements are used when possible. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Myopia is a rapidly increasing global ocular condition, largely attributed to prolonged engagement in near-work activities, 1 reduced outdoor exposure, 2 and genetic predisposition. 3 Its escalating prevalence represents a major public health concern, not only because more individuals experience visual impairment requiring frequent ophthalmic care, but also due to of the lifelong risk of sight-threatening pathologies associated with myopia. 4 These complications can ultimately lead to severe visual loss and reduced quality of life. 5,6 Myopia progression is intrinsically linked to axial elongation of the eye, 7 which strongly correlates with visual impairment. 4 Consequently, axial length (AL) measurement is a key biometric parameter for monitoring refractive development. 8 The ratio of AL to anterior corneal radius also correlates strongly with spherical equivalent (SE) refractive error 9 and may serve as an indicator for detecting myopia onset. 10 Axial length can be measured using ultrasound or optical coherence biometers. 11 While ultrasound requires corneal contact and local anaesthesia, optical biometers—based on interferometric or coherence-based technologies—offer superior precision and accuracy, achieving approximately ± 0.1 mm (≈ 0.25 D) even in paediatric populations. 12 Despite their utility, particularly in cataract surgery planning, optical biometers are mainly available in tertiary centres. In contrast, myopia detection and monitoring typically occur in primary care settings where such devices are less accessible. 13 To address the lack of direct AL measurement in primary care, several studies have developed theoretical 14 and statistical 13,15–21 models to predict AL using routinely obtained parameters. Common predictors include SE and corneal curvature (keratometry), with some models incorporating demographic or biometric variables such as age, sex, 16,17,20 weight 19 and anterior chamber depth. 21 However, differences in population characteristics, measurement devices, and testing conditions (e.g., cycloplegic vs. non-cycloplegic refraction) can affect model accuracy and agreement with measured AL. Among these predictors, SE is particularly sensitive to accommodation. When measured without cycloplegia, accommodative effort induces a transient myopic shift that may lead to overestimated AL predictions. 22,23 Morgan et al. reported only a marginal difference in the limits of agreement when comparing predictions based on cycloplegic (± 0.73 mm) and non-cycloplegic (± 0.75 mm) data, 13 while Tang et al. reported a decrease in the difference between predicted and measured AL as well as an improvement in the range of agreement. 16 To the best of our knowledge, no previous study has evaluated these predictive models within the same population. The present study assesses the accuracy of multiple AL prediction models under cycloplegic and non-cycloplegic conditions in a shared paediatric cohort. Additionally, it examines the intrasession repeatability of these models when refraction and keratometry are measured sequentially. Methods Literature Search A comprehensive literature search was conducted in PubMed, Web of Science, Scopus, and Cochrane databases for studies published between 2000 and 2025 on AL prediction formulas. Models were selected if they used input variables routinely measured in clinical practice — SE, keratometry, age, and sex. The search strategy combined terms related to AL, prediction or modelling, and clinical parameters. Only models based on optical coherence interferometry were included. The selected prediction models are summarised in Table 1 . Table 1. Formulas for predicting axial length using the input variables SE, K mean , Sex or Age. Participants This study included the same cohort of ninety-six paediatric participants previously described in detail in a previous study. 24 That study evaluated the repeatability and agreement of Myopia Master measurements under cycloplegic and non-cycloplegic conditions. The present work addresses a distinct question — the evaluation of AL prediction models using this dataset. The original measurement procedures, including the cycloplegia protocol, measurement sequence, fixation target, and quality control criteria, followed the validated methodology reported by Peñaranda et al. and are summarised below. The ninety-six participants (48 females; mean age 12.5 ± 2.4 years-old, range 7–16 years-old) underwent a comprehensive ophthalmologic examination at Clínica Oftalmológica Vista Sánchez-Trancón (Spain). Inclusion criteria were refractive astigmatism < 2.50 D under cycloplegia, distance-corrected visual acuity of 6/6 or better, and absence of ocular pathology or strabismus. The examination protocol included assessments of distance visual acuity (with and without correction), autorefraction, anterior corneal curvature measurements, AL measurement, objective refraction (with and without cycloplegia), subjective refraction, cover test, slit-lamp biomicroscopy, and fundus examination. Autorefraction, keratometry, and AL were measured using the Myopia Master (version 7.2 R3; Oculus, Germany), in "Myopia Mode". The device incorporates a fixation target simulating optical infinity with a fogging system to control accommodation. Measurements were performed automatically and sequentially, following manufacturer's protocol. Only measurements meeting predefined quality thresholds were included (quality index ≥ 7 for autorefraction and keratometry; signal-to-noise ratio ≥ 6.0 for AL). Before cycloplegia, autorefraction, anterior corneal curvature, and AL were each measured twice within a five-minute interval to assess intrasession repeatability. Cycloplegia was induced with one drop of cyclopentolate (1% cyclopentolate Colircusí, Alcon), followed by a second drop after 10 minutes. Post-cycloplegia measurements were performed 30 minutes after the initial instillation and were repeated twice within five minutes. All measurements were performed by the same experienced optometrist to ensure procedural consistency. The SE was calculated as sphere + (–cylinder)/2 and mean anterior corneal curvature (K mean ) as the average of the flat and steep meridians. Only right-eye data were analysed. The study adhered to the Declaration of Helsinki, was approved by the local ethics committee (Comité Ético para Investigación Clínica de Badajoz), and written informed consent was obtained from parents or legal guardians. Data and Statistical Analysis All statistical analyses were performed using JupyterLab (v4.0.11; https://jupyter.org ). Descriptive statistics are reported as mean ± standard deviation (M ± SD), together with range and 95% confidence interval (95% CI), same for mean difference (MD). Data normality was assessed using the Kolmogorov–Smirnov test. Participants were classified as myopic (SE ≤ − 0.50 D), emmetropic (–0.50 D < SE < + 1.00 D), or hyperopic (SE ≥ + 1.00 D) based on cycloplegic SE. Repeatability Analysis Intrasession repeatability of AL measurements—both directly measured and model-predicted using models — was evaluated using three complementary metrics. First, the within-subject standard deviation (Sw) quantified the variability between repeated measurements for each participant. Second, the coefficient of repeatability (CR) was calculated as 2.77 × Sw, with its 95% CI estimated from the chi-squared distribution. This coefficient represents the smallest detectable change that can be interpreted as exceeding expected measurement noise under identical conditions. Third, the intraclass correlation coefficient (ICC) was computed using a two-way random-effects model for absolute agreement [ICC(2,1)], accounting for variability attributable to both participants and repeated measures. ICC were interpreted as follows: 0.90, excellent). 25 This approach is particularly suitable for assessing the reliability of repeated measurements obtained with the same device under consistent testing conditions. Differences between repeated measurements were analysed using two-tailed paired t-tests. To the inflation of type I error arising from multiple comparisons across the seven predictive models and the measured AL, the significance threshold was adjusted using Bonferroni correction (α = 0.05/8 = 0.006). Agreement Analysis Agreement between measured and model-predicted AL was evaluated under both cycloplegic and non-cycloplegic conditions. For each participant, the reference AL was defined as the mean of two repeated measurements. Predicted AL values were derived using each model’s formula, with SE and K mean from the same measurement condition. Agreement was quantified by the mean difference between measured and predicted AL values, representing systematic bias. The 95% limits of agreement (LoA) were defined as the mean difference ± 1.96 times the SD of the paired differences, indicating the interval within which 95% of the differences are expected to lie. Confidence intervals for the LoA were also computed to reflect estimate precision. The intraclass correlation coefficient [ICC(3,1)] was used to assess relative reliability—that is, the consistency with which measured and predicted AL values rank across participants, irrespective of systematic bias. The coefficient of variation within subjects (CV WS ) expressed as a percentage, was calculated as the within-subject SD divided by the mean AL. Together, these metrics provided a scale-independent assessment of prediction reliability. Paired t -tests were used to detect systematic bias between predicted and measured AL. Significance was again adjusted using the Bonferroni correction (α = 0.05/7 = 0.007). Influence of Input Variation on Model-Predicted AL The effect of variability in pre-cycloplegic inputs on predicted AL was examined using multiple linear regression, with post-cycloplegic measurements serving as the reference standard. The dependent variable was the difference between pre- and post-cycloplegic predicted AL (ΔAL). Independent variables included changes in SE (ΔSE) and mean corneal curvature (ΔKmean), while static factors (e.g., age, gender) were excluded. These ΔAL were then regressed against the corresponding differences in SE (ΔSE) and K mean (ΔK mean ), allowing to determine the extent to which variability in each parameter contributed to deviations in model-predicted AL relative to its cycloplegic baseline. Statistical Power Calculation The study had 88% power to detect a mean difference smaller than 0.2 mm between measured and predicted AL, assuming a standard deviation of 0.5 mm and a Bonferroni-adjusted significance level of 0.007 (0.05/7). A difference of 0.2 mm corresponds to approximately 0.50D, which is considered clinically relevant in paediatric myopia management. Results The group comprised 35 myopes, SE = − 2.48 ± 1.24 D (range: −5.64 to − 0.79 D), 30 emmetropes, SE = + 0.29 ± 0.48 D (range: −0.66 to + 0.95 D), and 31 hyperopes, SE = + 2.48 ± 1.56 D (range: +1.00 to + 7.25 D), Table 2 . Comparison of pre- and post-cycloplegic data showed statistically significant SE shifts toward more positive refractions in emmetropes, hyperopes, and the combined group. Mean differences were − 0.59 ± 0.12 D (95% CI: −0.83 to − 0.34, p < 0.001) for emmetropes, − 1.47 ± 0.39 D (95% CI: −2.26 to − 0.68, p < 0.001) for hyperopes, and + 0.79 ± 0.32 D (95% CI: 0.16 to 1.42, p = 0.014) for the overall sample. Myopes showed no significant change (MD = 0.38 ± 0.30 D; 95% CI: −0.98 to 0.22; p = 0.204). Anterior corneal curvature and axial length differences were not statistically significant (p > 0.203). Table 2 Demographic, refractive and biometric data pre- and post-cycloplegia, represented by mean ± standard deviation and range. Pre-cycloplegia* M ± SD (range) Post-cycloplegia* M ± SD (range) Patients n SE(D) K mean (mm) AL (mm) SE(D) K mean (mm) AL(mm) Myopes 35 -2.71 ± 1.30 (-6.27 to -0.72) 7.77 ± 0.25 (7.20 to 8.35) 24.40 ± 0.97 (22.05 to 26.68) -2.33 ± 1.29 (-5.64 to -0.54) 7.78 ± 0.25 (7.19 to 8.37) 24.40 ± 0.96 (22.05 to 26.70) Emmetropes 30 -0.20 ± 0.47 ‡ (-1.36 to 0.59) 7.77 ± 0.26 (7.31 to 8.36) 23.26 ± 0.69 (22.26 to 24.40) 0.39 ± 0.41 ‡ (-0.42 to 0.95) 7.77 ± 0.25 (7.31 to 8.39) 23.28 ± 0.69 (22.27 to 24.46) Hyperopes 31 1.01 ± 1.48 ‡ (-1.54 to 5.84) 7.82 ± 0.32 (7.11 to 8.46) 22.34 ± 0.84 (20.43 to 23.94) 2.48 ± 1.56 ‡ (1.00 to 7.25) 7.82 ± 0.33 (7.11 to 8.50) 22.35 ± 0.87 (20.41 to 24.07) All 96 -0.80 ± 2.00 † (-6.27 to 5.84) 7.79 ± 0.27 (7.11 to 8.46) 23.41 ± 1.21 (20.43 to 26.68) -0.01 ± 2.37 † (-5.64 to 7.25) 7.79 ± 0.28 (7.11 to 8.50) 23.42 ± 1.22 (20.41 to 26.70) * - Average of two measurements; M – Mean; SD – Standard deviation; SE – Spherical equivalent; K mean – Mean anterior corneal curvature; AL – Axial length. † - Paired t-test; p = 0.014; ‡ - Paired t-test; p < 0.001. Intrasession repeatability of measured and predicted AL Table 3 shows that the MD between consecutive AL measurements and predicted AL derived from consecutive SE and K mean was close to zero, pre- and post-cycloplegia. Measured AL demonstrated the lowest CR pre-cycloplegia (0.14 mm, 95% CI: 0.13 to 0.17mm) and post-cycloplegia (0.09 mm, 95% CI: 0.08 to 0.11mm), outperforming all AL predictive models. Among the models, Tang et al. achieved the best repeatability pre-cycloplegia (0.24 mm, 95% CI: 0.21 to 0.28mm) and post-cycloplegia (0.16 mm, 95% CI: 0.14 to 0.18), whereas He et al. showed the poorest pre-cycloplegia (0.48 mm, 95% CI: 0.42 to 0.56 mm) and post-cycloplegia (0.27 mm, 95% CI: 0.24 to 0.32mm). Overall, both measured and predicted AL improved in CR repeatability after cycloplegia. Intraclass correlation for measured and predicted AL were excellent under both conditions (ICC > 0.99, with a range of the 95%LoA of 0.98 to 1.00). Table 3 Intrasession repeatability of measured and predicted AL. Pre-Cycloplegia Method Measure 1 M ± SD (mm) Measure 2 M ± SD (mm) MD ± SD (mm) (95% CI) CR (mm) (95% CI) Measured AL 23.41 ± 1.22 23.41 ± 1.21 -0.00 ± 0.07 * (-0.02 to 0.01) 0.14 (0.13 to 0.17) He et al. 23.88 ± 1.67 23.89 ± 1.65 -0.01 ± 0.24 * (-0.06 to 0.04) 0.48 (0.42 to 0.56) Kim et al. 24.28 ± 1.11 24.29 ± 1.11 -0.01 ± 0.14 * (-0.04 to 0.02) 0.28 (0.25 to 0.33) Tang et al. 24.11 ± 0.91 24.11 ± 0.91 -0.01 ± 0.12 * (-0.03 to 0.02) 0.24 (0.21 to 0.28) Morgan et al. 23.65 ± 0.95 23.66 ± 0.95 -0.01 ± 0.13 * (-0.03 to 0.02) 0.25 (0.22 to 0.29) Queirós et al. 23.69 ± 1.05 23.70 ± 1.04 -0.01 ± 0.15 * (-0.04 to 0.03) 0.30 (0.26 to 0.35) Dutt et al. 23.97 ± 0.97 23.97 ± 0.96 -0.01 ± 0.14 * (-0.03 to 0.02) 0.27 (0.24 to 0.32) Lingham et al. 23.87 ± 1.06 23.88 ± 1.05 -0.01 ± 0.14 * (-0.04 to 0.02) 0.28 (0.25 to 0.33) Post-Cycloplegia Method Measure 1 M ± SD (mm) Measure 2 M ± SD (mm) MD ± SD (mm) (95% CI) CR (mm) (95% CI) Measured AL 23.42 ± 1.22 23.42 ± 1.22 0.00 ± 0.05 * (-0.01 to 0.01) 0.09 (0.08 to 0.11) He et al. 23.32 ± 1.86 23.32 ± 1.87 -0.01 ± 0.14 * (-0.04 to 0.02) 0.27 (0.24 to 0.32) Kim et al. 23.97 ± 1.20 23.98 ± 1.21 -0.01 ± 0.10 * (-0.03 to 0.01) 0.20 (0.17 to 0.23) Tang et al. 23.83 ± 1.00 23.84 ± 1.01 -0.01 ± 0.08 * (-0.02 to 0.01) 0.16 (0.14 to 0.18) Morgan et al. 23.36 ± 1.02 23.37 ± 1.03 -0.01 ± 0.08 * (-0.02 to 0.01) 0.16 (0.14 to 0.18) Queirós et al. 23.35 ± 1.17 23.36 ± 1.18 -0.01 ± 0.09 * (-0.02 to 0.01) 0.18 (0.160 to 0.21) Dutt et al. 23.65 ± 1.08 23.65 ± 1.09 -0.01 ± 0.09 * (-0.02 to 0.01) 0.17 (0.15 to 0.20) Lingham et al. 23.54 ± 1.16 23.54 ± 1.17 -0.01 ± 0.09 * (-0.02 to 0.01) 0.17 (0.15 to 0.20) *p > 0.492 for all; M –mean; SD – standard deviation; MD – mean difference; CR – coefficient of repeatability; CI –confidence intervals; Agreement between measured and predicted AL Pre-cycloplegia, all predictive models significantly overpredicted measured AL (p < 0.001 for all; Table 4 and Fig. 1 ). The largest bias was observed with the Kim et al. model (MD=-0.87 ± 0.60 mm, 95% CI: -0.99 to − 0.75 mm), and the smallest with Morgan et al. predictive model (MD=-0.24 ± 0.55 mm, 95% CI: -0.35 to − 0.13 mm). Kim et al. also showed the widest LoA (-2.05 to + 0.30 mm) and the highest CV ws (3.13%), whereas Queirós et al. exhibited the narrowest LoA (-1.31 to -0.74 mm) and the lowest CV ws (1.78%). Intraclass correlations indicated good-to-excellent reliability (all ICC ≧ 0.81). Table 4 Agreement between measured and estimated AL. Pre-Cycloplegia Prediction Model AL Predicted AL Measured MD ± SD (mm) (95% CI) Lower LoA (mm) (95% CI) Upper LoA (mm) (95% CI) CV WS ‡ (%) ICC § (95%LoA) M ± SD (mm) He et al. 23.88 ± 1.65 23.41 ± 1.21 -0.47 ± 0.78 * (-0.63 to − 0.31) -1.99 (-2.26 to -1.81) 1.05 (0.87 to 1.32) 2.70 0.90 (0.76 to 0.95) Kim et al. 24.28 ± 1.11 -0.87 ± 0.60 * (-0.99 to − 0.75) -2.05 (-2.25 to − 1.90) 0.30 (0.16 to 0.51) 3.13 0.81 (-0.12 to 0.94) Tang et al. 24.11 ± 0.90 -0.70 ± 0.55 * (-0.81 to − 0.59) -1.78 (-1.96 to-1.65) 0.39 (0.25 to 0.56) 2.64 0.84 (0.02 to 0.95) Morgan et al. 23.65 ± 0.95 -0.24 ± 0.55 * (-0.35 to − 0.13) -1.33 (-1.50 to − 1.19) 0.84 (0.71 to 1.02) 1.81 0.92 (0.85 to 0.95) Queirós et al. 23.69 ± 1.04 -0.28 ± 0.52 * (-0.39 to-0.18) -1.31 (-1.47 to − 1.18) 0.74 (0.62 to 0.91) 1.78 0.93 (0.85 to 0.96) Dutt et al. 23.96 ± 0.96 -0.56 ± 0.55 * (-0.67 to − 0.45) -1.63 (-1.82 to − 1.59) 0.51 (0.39 to 0.70) 2.32 0.88 (0.34 to 0.95) Lingham et al. 23.87 ± 1.05 -0.46 ± 0.52 * (-0.57 to − 0.35) -1.49 (-1.65 to − 1.36) 0.57 (0.44 to 0.73) 2.08 0.91 (0.59 to 0.96) Post-Cycloplegia Prediction Model AL Predicted AL Measured MD ± SD (mm) (95% CI) Lower LoA (mm) (95% CI) Upper LoA (mm) (95% CI) CV WS ‡ (%) ICC § (95%LoA) M ± SD (mm) He et al. 23.32 ± 1.86 23.42 ± 1.22 0.10 ± 0.81** (-0.06 to + 0.26) -1.49 (-1.76 to -1.30) 1.69 (1.50 to 1.96) 2.47 0.93(0.89 to 0.95) Kim et al. 23.98 ± 1.21 -0.56 ± 0.43 * (-0.65 to -0.47) -1.40 (-1.55 to -1.30) 0.28 (0.18 to 0.43) 2.09 0.92 (0.22 to 0.98) Tang et al. 23.42 ± 1.22 -0.41 ± 0.38* (-0.49 to − 0.33) -1.15 (-1.28 to − 1.07) 0.33 (0.25 to 0.46) 1.68 0.94 (0.53 to 0.98) Morgan et al. 23.36 ± 1.03 0.06 ± 0.40 ** (-0.02 to 0.14) -0.72 (-0.86 to − 0.63) 0.84 (0.75 to 0.98) 1.21 0.97 (0.95 to 0.98) Queirós et al. 23.35 ± 1.17 0.07 ± 0.36 ** (0.0 to 0.14) -0.64 (-0.76 to − 0.55) 0.78 (0.69 to 0.90) 1.11 0.98 (0.96 to 0.98) Dutt et al. 23.65 ± 1.08 -0.23 ± 0.38 * (-0.31 to -0.15) -0.89 (-1.10 to − 0.97) 0.51 (0.43 to 0.64) 1.33 0.96 (0.91 to 0.98) Lingham et al. 23.54 ± 1.17 -0.12 ± 0.35* (-0.19 to -0.05) -0.81 (-0.92 to − 0.72) 0.57 (0.48 to 0.68) 1.12 0.98 (0.96 to 0,98) *Statistically significant at p 0.05; § Two-way random-effects model. M ± SD - Mean ± standard deviation; MD ± SD - mean difference ± SD of the differences; LoA – Limits of Agreement; CV WS - coefficient of variation; ICC- Intraclass correlation coefficients; CI – Confidence Intervals Post-cycloplegia, mean differences between predicted and measured AL decreased across all models. Kim et al retained the largest bias (MD = − 0.56 ± 0.43 mm, 95% CI: − 0.65 to − 0.47mm), while Morgan et al. showed the smallest (MD = 0.06 ± 0.40 mm, 95% CI: − 0.02 to 0.14 mm). He et al. had the widest LoA (–1.49 to 1.69 mm) and highest CV ws (2.47%), while Lingham et al. demonstrated the narrowest LoA (–0.81 to 0.57 mm) and one of the lowest CV ws (1.12%). Reliability improved further (ICC ≥ 0.92; 95% LoA: − 0.12 to 0.94 mm), post-cycloplegia. A consistent proportional bias was found, shorter eyes (hyperopic and emmetropic) were overpredicted, while myopic eyes showed near-accurate estimates, except in the He et al. model, which displayed the opposite pattern. Grouped AL analysis confirmed that cycloplegia reduced overestimation in hyperopic and emmetropic eyes, while myopes tended to maintain the underestimation, Fig. 2 . The proportion of eyes with prediction errors 97% of eyes, Fig. 3 . Influence of SE and K mean Cycloplegia Variation on Model-Predicted AL Figure 4 illustrates the relationship between cycloplegia-induced changes in spherical equivalent (ΔSE) and mean corneal curvature (ΔK mean ) with variation in predicted axial length (ΔAL). Across all models, ΔSE— defined as post- minus pre-cycloplegic SE —was the main driver of ΔAL variation, whereas ΔKmean had minimal impact. Multivariate linear regression (Table 5 ) confirmed this trend, with all models statistically significant, explaining 98.8–99.7% of the variance in ΔAL. The contribution of ΔSE accounted for 97.3–99.1% of the explained variance, while ΔKmean contributed only 0.5–1.2%. These findings demonstrate that cycloplegia-induced changes in SE are the principal determinant of variation in predicted AL, whereas alterations in corneal curvature play a negligible role. Table 5 Multivariate linear regression parameters for AL prediction error (ΔAL = AL pre-cycloplegia – post-cycloplegia)., respectively. Model β 1 β 2 Error Adjusted R² p-value He et al. -0.997 -0.073 0.036 0.996 < 0.001 Kim et al. -0.989 -0.139 0.030 0.992 < 0.001 Tang et al. -0.994 -0.111 0.020 0.995 < 0.001 Morgan et al. -0.990 -0.102 0.033 0.987 < 0.001 Queirós et al. -0.996 -0.093 0.022 0.996 < 0.001 Dutt et al. -0.996 -0.093 0.020 0.996 < 0.001 Lingham et al. -0.995 -0.084 0.027 0.994 < 0.001 β 1 and β 2 are the regression coefficients of ΔSE (pre-cycloplegia – post-cycloplegia); ΔK mean (pre-cycloplegia - post-cycloplegia) Discussion This study compared the performance of several AL prediction models in a paediatric population, before and after cycloplegia. The models were based on linear regression incorporating ocular (SE and K mean ) and demographic parameters (age and sex), with SE showing a strong association with AL. 9 It was hypothesized that variability in SE measurements could affect model performance. The findings indicate that, across all models, AL prediction accuracy and repeatability improved following cycloplegia, with five of the seven models showing LoA for AL prediction within 1.0 mm of the measured values after cycloplegia. Cycloplegia shifted the refractive error towards more positive values (+ 0.79 D), with the effect being more pronounced in emmetropes (+ 0.59 D) and hyperopes (+ 1.47 D). 24 This shift is consistent with values reported by Hu et al. (+ 0.78 ± 0.79 D) in children aged 4–18 years 26 and large-scale studies in 12-year-olds (+ 0.84 D). 22 This behaviour confirms the greater accommodative effort of emmetropic and hyperopic eyes during autorefraction. 23 Differences in K mean and AL remained within instrument repeatability limits, indicating that these anatomical parameters are unaffected by cycloplegia. 24,27 Before cycloplegia, all models overpredicted AL, with mean differences ranging from − 0.87 mm (Kim et al.) 14 to − 0.24 mm (Morgan et al.) 13 , and LoA extending up to 1.18 mm from the mean. In the He et al. 15 model, LoA were even broader, spanning 1.53 mm. After cycloplegia, prediction errors decreased: the Kim et al. 14 model overpredicted by − 0.56 mm, whereas the He et al. 15 model slightly underpredicted (+ 0.10 mm). Models by Morgan et al. 13 and Queirós et al. 17 showed mean differences below 0.10 mm, corresponding to refractive errors under 0.25 D according to the Gullstrand model eye. LoA also narrowed (< 0.84 mm) for all models except He et al., 15 whose precision remained similar to pre-cycloplegia conditions. Using the relationship of 0.4 mm axial elongation per 1.0 D, 28 the LoA still represent potential differences of about 2.0 D —highlighting that AL estimations from predictive models should be interpreted cautiously. Bland-Altman and regression analyses (Fig. 1 ) show that most models (except He et al.) 15 tended to overpredict AL in emmetropic and hyperopic eyes and underpredict in myopic eyes. Grouping eyes by AL range (Fig. 2 ) revealed that prediction errors were greatest in shorter eyes (hyperopic and emmetropic) and reduced post-cycloplegia. Myopic eyes showed smaller, more stable errors. Post-cycloplegia, especially for the Queirós et al. and Lingham et al. models, 50% of eyes with AL between 21–26 mm clustered around the zero-error line, with 95% of cases within 1.0 mm. The reduction in proportional bias post-cycloplegia observed in the Bland–Altman analysis appears particularly relevant in eyes with more extreme axial lengths. The performance of a prediction model depends on its developmental methodology, the population used, and the independent variables selected. These factors influence the model’s generalizability. All reviewed models share a strong dependence of AL on SE and anterior corneal curvature, grounded in the principles of ocular refraction. He, Tang, Queirós and Lingham et al. 15–17,20 further incorporated age and sex, which improved model fit and are known to influence AL. He et al. demonstrated that ~ 83% of SE variance is explained by the AL/ K mean ratio. 15 They developed an SE prediction model based on AL, K mean , and sex, later reformulated here to predict AL. Their sample comprised 3922 Asian children aged 6–12 years-old with cycloplegic refraction. The model showed underprediction in hyperopes and overprediction in myopes—opposite to other models—likely because their SE model estimated more positive SE in hyperopes and more negative SE in myopes. Sex-related differences in AL (males: +0.44 mm) and Kmean (females: +0.13 mm) were consistent with our data (AL: +0.70 mm; K mean : +0.19 mm, data not shown), suggesting that including sex can enhance performance. Kim et al. derived a model from the simplified Gullstrand eye, using Kmean and SE with a correction constant for SE variation. 14 Their 382-participant Asian cohort (ages 7–77 years) was measured without cycloplegia. They reported an AL overprediction of 0.18 ± 0.47 mm (95% LoA: − 0.75 to + 1.10 mm), with myopes showing the largest errors. In our cohort, pre-cycloplegia overprediction was higher (0.87 ± 0.60 mm; 95% LoA: − 2.05 to + 0.30 mm), especially in hyperopic and emmetropic eyes. Kim et al. reported 75.5% and 95.5% of predictions within 0.5 mm and 1.0 mm, respectively—substantially higher than our findings (23% ≤ 0.5 mm; 61% ≤ 1.0 mm). These differences likely reflect cohort characteristics, since age and high ametropia reduce model precision. 20 Tang et al. developed an AL prediction model using linear regression and machine learning on 1011 myopic Asian children aged 6–18 years-old, with cycloplegic refraction. Their predictors (K mean , SE, sex, and age) explained 81% of AL variance—comparable to 82% (pre-) and 92% (post-cycloplegia) in this study. The inclusion of age accounted for AL elongation during childhood and adolescence and the concurrent reduction in lens power. 29–31 Morgan et al. proposed a linear regression model based on cycloplegic SE and K mean in 144 Caucasian participants aged 8–12 years-old, later validated in 1046 individuals aged 6–22 years. 13 They reported an AL underestimation of 0.13 mm (95% LoA: − 0.73 to + 0.99 mm), consistent with our post-cycloplegia results (0.06 mm; 95% LoA: − 0.72 to + 0.84 mm). Queirós et al. applied this model to 1783 participants aged 6–25 years-old (SE measured with an open-field autorefractor) and found an AL overestimation of 0.25 ± 0.48 mm (95% LoA: +0.70 to + 1.20 mm), aligning with our pre-cycloplegia data. 17 Queirós et al proposed a new model, adding age as a predictor, which explained ~ 80% of AL variability. In the preset study, this model showed an 0.28 mm overprediction (one of the lowest) pre-cycloplegia and an 0.08 mm underprediction after cycloplegia—supporting the role of accommodation control in improving AL prediction. Dutt et al. developed a regression model using cycloplegic SE and K mean from 1301 Caucasian adults aged 18–22 years-old. 18 Under non-cycloplegic and cycloplegic conditions, their model overestimated AL by 0.10 ± 0.52 mm (95% LoA: − 0.92 to + 0.11 mm) and 0.01 ± 0.49 mm (95% LoA: − 0.94 to + 0.97 mm), respectively. In our cohort, overestimations were greater (pre-cycloplegia: − 0.56 ± 0.55 mm; post-cycloplegia: − 0.23 ± 0.38 mm), reaffirming that cycloplegia enhances precision and accuracy. Lingham et al. proposed a regression model based on cycloplegic SE, K mean , sex, and age, trained on 1068 Caucasians (6–20 years-old), 3429 Asians (5–18 years-old), and 240 Caucasian myopes (6–19 years-old). Their model underpredicted AL by 0.08 ± 0.40 mm (95% LoA: − 0.71 to + 0.87 mm) in a myopic test set. In our cohort after cycloplegia, it slightly overpredicted AL (–0.12 ± 0.35 mm; 95% LoA: − 0.81 to + 0.57 mm), likely due to inclusion of hyperopic and emmetropic eyes. Regression analysis of AL prediction errors against cycloplegic variations in Kmean and SE showed that changes in spherical equivalent (ΔSE) explained 97.3%–99.1% of the variance in predicted AL across all models, whereas mean corneal curvature (ΔKmean) contributed minimally (0.5%–1.2%). Considering the reported repeatability of approximately 0.65 D for non-cycloplegic objective refraction, 0.32 D after cycloplegia, 24 and 0.35 D for subjective refraction, 32 the resulting prediction error may reach ~ 0.15 mm, limiting the detection of subtle AL changes. These findings confirm that precise SE measurement is the primary determinant of axial length estimation accuracy and repeatability. A major strength of this study lies in the direct comparison of multiple AL prediction models on the same paediatric cohort, both before and after cycloplegia. Since these models rely on routinely acquired clinical parameters, the findings offer practical insights into their clinical applicability. The inclusion of a balanced distribution of refractive error types allows for a broader and more representative comparison than those of Tang et al. and Lingham et al., which were limited to myopic eyes. In the context of myopia progression, monitoring AL in emmetropic and low-hyperopic eyes is clinically relevant, as a reduction in hyperopia may indicate axial elongation. 30 However, some limitations should be acknowledged. The sample size was smaller than those in the original model development studies. Nonetheless, this study aimed to compare rather than validate or train new models, for which the sample size was adequate. Additionally, only paediatric Caucasian participants were included, whereas most existing models were derived from Asian or mixed cohorts. Although ethnic differences in AL have been, 33 they appear to stem mainly from ocular biometric relationships rather than ethnicity itself, 20 supporting the validity of our comparisons while underscoring the need for broader cross-population assessments. Conclusions This study highlights the importance of cycloplegic refraction for improving the accuracy and repeatability of AL predictive models. The models of Morgan et al., 13 Queirós et al., 17 and Lingham et al. 20 demonstrated minimal bias and superior repeatability under cycloplegic conditions. SE was the primary factor influencing prediction errors, while corneal curvature had negligible impact. Overall, AL predictions were more accurate in myopic eyes than in hyperopic or emmetropic eyes, where mild overprediction persisted. These models provide a useful alternative in primary care settings without optical biometers, particularly for paediatric myopia management, though they should not replace direct AL measurements when available. Declarations Author Contribution I. S. and P.S. performed the conceptualization, formal analysis, methodology, software, supervision, prepared figures and wrote the main manuscript text. A.B. performed formal analysis, supervision, methodology and wrote the main manuscript text. O.T. performed the conceptualization, data curation and validation. All authors reviewed the manuscript Acknowledgement The authors want to thank Agustin Peñaranda for his role in the clinical data collection. Ethics approval This study was carried out in accordance with the principles of the Declaration of Helsinki, and was approved by the local ethics committee (Comité Ético para Investigación Clínica de Badajoz). Consent to participate A written informed consent for each participant was obtained from parents or legal guardians. Funding Open access funding provided by FCT|FCCN (b-on). The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Disclosure statement No potential conflict of interest was reported by the author(s) ORCID Ivo Soares http://orcid.org/0000-0001-6712-7514 António Baptista https://orcid.org/0000-0002-7304-8756 Oscar Torrado https://orcid.org/0000-0001-5808-7602 Pedro Serra https://orcid.org/0000-0003-0471-0213 References 1. Gajjar S, Ostrin LA. A systematic review of near work and myopia: measurement, relationships, mechanisms and clinical corollaries. Acta Ophthalmol . 2022;100(4):376–387. doi:10.1111/aos.15043 2. Xiong S, Sankaridurg P, Naduvilath T, et al. Time spent in outdoor activities in relation to myopia prevention and control: a meta-analysis and systematic review. 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08:51:47","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116172,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7974559/v1/55448eec02fd5c06a1ec82d4.html"},{"id":95810118,"identity":"78f590c7-e2fb-4ec9-a730-8c136959a041","added_by":"auto","created_at":"2025-11-13 08:51:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13118649,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of AL prediction against measured AL (n = 96). Red, green and blue dots indicate myopic, emmetropic and hyperopic participants, respectively. The first and second column shows the Bland-Altman plots for pre- and post-cycloplegia, respectively. The black solid line represents the mean difference between measured and predicted values (measured – predicted). The black dashed lines are the 95% limits of agreement. The third and fourth column shows the Pearson correlation analysis of predicted and measured AL values for pre- and post-cycloplegia, respectively. The solid black lines represent the regression model and the dashed line the identity line (y = x).\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7974559/v1/2d35481e61be4a2019088d3c.jpeg"},{"id":95810115,"identity":"84fef4a4-05c4-436e-93eb-0f345669d2c3","added_by":"auto","created_at":"2025-11-13 08:51:49","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2726293,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated AL error for each predictive model in pre- (top row) and post-cycloplegia (bottom row) for several AL intervals (negative values indicate model overestimation). Red, green and blue dots indicate myopic, emmetropic and hyperopic participants, respectively.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7974559/v1/bc7f92f785868f612f4f5509.jpeg"},{"id":95810170,"identity":"82423bdd-20cd-47e5-9554-8aa3d77d514b","added_by":"auto","created_at":"2025-11-13 08:51:55","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5716435,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of eyes with axial length prediction below thresholds, pre-cycloplegia (top row) and post-cycloplegia (bottom row).\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7974559/v1/b4dbb68733b3a585676623d7.jpeg"},{"id":95810175,"identity":"bf69c133-2006-4ea3-9a98-10b1c3e49fa0","added_by":"auto","created_at":"2025-11-13 08:51:56","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5957630,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between SE measurement error (ΔSE = pre - post) and AL prediction error (ΔAL = pre - post) (top row) and between K\u003csub\u003emean\u003c/sub\u003e measurement error (ΔK\u003csub\u003emean\u003c/sub\u003e = pre- post) and ΔAL (bottom row).\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7974559/v1/00fb7ab9d8931fb668712c68.jpeg"},{"id":95818940,"identity":"093c9c5d-2881-4606-85be-3cc83964119f","added_by":"auto","created_at":"2025-11-13 10:35:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":28884997,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7974559/v1/a7a87131-d192-41b2-8bbd-924bcc34663d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of cycloplegia on the axial length prediction models in a peadiatric cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMyopia is a rapidly increasing global ocular condition, largely attributed to prolonged engagement in near-work activities,\u003csup\u003e1\u003c/sup\u003e reduced outdoor exposure,\u003csup\u003e2\u003c/sup\u003e and genetic predisposition.\u003csup\u003e3\u003c/sup\u003e Its escalating prevalence represents a major public health concern, not only because more individuals experience visual impairment requiring frequent ophthalmic care, but also due to of the lifelong risk of sight-threatening pathologies associated with myopia.\u003csup\u003e4\u003c/sup\u003e These complications can ultimately lead to severe visual loss and reduced quality of life.\u003csup\u003e5,6\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eMyopia progression is intrinsically linked to axial elongation of the eye,\u003csup\u003e7\u003c/sup\u003e which strongly correlates with visual impairment.\u003csup\u003e4\u003c/sup\u003e Consequently, axial length (AL) measurement is a key biometric parameter for monitoring refractive development.\u003csup\u003e8\u003c/sup\u003e The ratio of AL to anterior corneal radius also correlates strongly with spherical equivalent (SE) refractive error\u003csup\u003e9\u003c/sup\u003e and may serve as an indicator for detecting myopia onset.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAxial length can be measured using ultrasound or optical coherence biometers.\u003csup\u003e11\u003c/sup\u003e While ultrasound requires corneal contact and local anaesthesia, optical biometers\u0026mdash;based on interferometric or coherence-based technologies\u0026mdash;offer superior precision and accuracy, achieving approximately\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 mm (\u0026asymp;\u0026thinsp;0.25 D) even in paediatric populations.\u003csup\u003e12\u003c/sup\u003e Despite their utility, particularly in cataract surgery planning, optical biometers are mainly available in tertiary centres. In contrast, myopia detection and monitoring typically occur in primary care settings where such devices are less accessible.\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTo address the lack of direct AL measurement in primary care, several studies have developed theoretical\u003csup\u003e14\u003c/sup\u003e and statistical\u003csup\u003e13,15\u0026ndash;21\u003c/sup\u003e models to predict AL using routinely obtained parameters. Common predictors include SE and corneal curvature (keratometry), with some models incorporating demographic or biometric variables such as age, sex,\u003csup\u003e16,17,20\u003c/sup\u003e weight\u003csup\u003e19\u003c/sup\u003e and anterior chamber depth.\u003csup\u003e21\u003c/sup\u003e However, differences in population characteristics, measurement devices, and testing conditions (e.g., cycloplegic vs. non-cycloplegic refraction) can affect model accuracy and agreement with measured AL.\u003c/p\u003e\u003cp\u003eAmong these predictors, SE is particularly sensitive to accommodation. When measured without cycloplegia, accommodative effort induces a transient myopic shift that may lead to overestimated AL predictions.\u003csup\u003e22,23\u003c/sup\u003e Morgan et al. reported only a marginal difference in the limits of agreement when comparing predictions based on cycloplegic (\u0026plusmn;\u0026thinsp;0.73 mm) and non-cycloplegic (\u0026plusmn;\u0026thinsp;0.75 mm) data,\u003csup\u003e13\u003c/sup\u003e while Tang et al. reported a decrease in the difference between predicted and measured AL as well as an improvement in the range of agreement.\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTo the best of our knowledge, no previous study has evaluated these predictive models within the same population. The present study assesses the accuracy of multiple AL prediction models under cycloplegic and non-cycloplegic conditions in a shared paediatric cohort. Additionally, it examines the intrasession repeatability of these models when refraction and keratometry are measured sequentially.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eLiterature Search\u003c/h2\u003e\n \u003cp\u003eA comprehensive literature search was conducted in PubMed, Web of Science, Scopus, and Cochrane databases for studies published between 2000 and 2025 on AL prediction formulas. Models were selected if they used input variables routinely measured in clinical practice \u0026mdash; SE, keratometry, age, and sex. The search strategy combined terms related to AL, prediction or modelling, and clinical parameters. Only models based on optical coherence interferometry were included. The selected prediction models are summarised in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Formulas for predicting axial length using the input variables SE, K\u003csub\u003emean\u003c/sub\u003e, Sex or Age.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1763021549.png\" width=\"747\" height=\"736\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThis study included the same cohort of ninety-six paediatric participants previously described in detail in a previous study.\u003csup\u003e24\u003c/sup\u003e That study evaluated the repeatability and agreement of Myopia Master measurements under cycloplegic and non-cycloplegic conditions. The present work addresses a distinct question \u0026mdash; the evaluation of AL prediction models using this dataset. The original measurement procedures, including the cycloplegia protocol, measurement sequence, fixation target, and quality control criteria, followed the validated methodology reported by Pe\u0026ntilde;aranda et al. and are summarised below.\u003c/p\u003e\n\u003cp\u003eThe ninety-six participants (48 females; mean age 12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 years-old, range 7\u0026ndash;16 years-old) underwent a comprehensive ophthalmologic examination at Cl\u0026iacute;nica Oftalmol\u0026oacute;gica Vista S\u0026aacute;nchez-Tranc\u0026oacute;n (Spain). Inclusion criteria were refractive astigmatism\u0026thinsp;\u0026lt;\u0026thinsp;2.50 D under cycloplegia, distance-corrected visual acuity of 6/6 or better, and absence of ocular pathology or strabismus.\u003c/p\u003e\n\u003cp\u003eThe examination protocol included assessments of distance visual acuity (with and without correction), autorefraction, anterior corneal curvature measurements, AL measurement, objective refraction (with and without cycloplegia), subjective refraction, cover test, slit-lamp biomicroscopy, and fundus examination.\u003c/p\u003e\n\u003cp\u003eAutorefraction, keratometry, and AL were measured using the Myopia Master (version 7.2 R3; Oculus, Germany), in \u0026quot;Myopia Mode\u0026quot;. The device incorporates a fixation target simulating optical infinity with a fogging system to control accommodation. Measurements were performed automatically and sequentially, following manufacturer\u0026apos;s protocol. Only measurements meeting predefined quality thresholds were included (quality index\u0026thinsp;\u0026ge;\u0026thinsp;7 for autorefraction and keratometry; signal-to-noise ratio\u0026thinsp;\u0026ge;\u0026thinsp;6.0 for AL).\u003c/p\u003e\n\u003cp\u003eBefore cycloplegia, autorefraction, anterior corneal curvature, and AL were each measured twice within a five-minute interval to assess intrasession repeatability. Cycloplegia was induced with one drop of cyclopentolate (1% cyclopentolate Colircus\u0026iacute;, Alcon), followed by a second drop after 10 minutes. Post-cycloplegia measurements were performed 30 minutes after the initial instillation and were repeated twice within five minutes. All measurements were performed by the same experienced optometrist to ensure procedural consistency.\u003c/p\u003e\n\u003cp\u003eThe SE was calculated as sphere + (\u0026ndash;cylinder)/2 and mean anterior corneal curvature (K\u003csub\u003emean\u003c/sub\u003e) as the average of the flat and steep meridians. Only right-eye data were analysed. The study adhered to the Declaration of Helsinki, was approved by the local ethics committee (Comit\u0026eacute; \u0026Eacute;tico para Investigaci\u0026oacute;n Cl\u0026iacute;nica de Badajoz), and written informed consent was obtained from parents or legal guardians.\u003c/p\u003e\n\u003ch3\u003eData and Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed using JupyterLab (v4.0.11; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jupyter.org\u003c/span\u003e\u003c/span\u003e). Descriptive statistics are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), together with range and 95% confidence interval (95% CI), same for mean difference (MD). Data normality was assessed using the Kolmogorov\u0026ndash;Smirnov test. Participants were classified as myopic (SE \u0026le; \u0026minus;\u0026thinsp;0.50 D), emmetropic (\u0026ndash;0.50 D\u0026thinsp;\u0026lt;\u0026thinsp;SE\u0026thinsp;\u0026lt;\u0026thinsp;+\u0026thinsp;1.00 D), or hyperopic (SE\u0026thinsp;\u0026ge;\u0026thinsp;+\u0026thinsp;1.00 D) based on cycloplegic SE.\u003c/p\u003e\n\u003ch3\u003eRepeatability Analysis\u003c/h3\u003e\n\u003cp\u003eIntrasession repeatability of AL measurements\u0026mdash;both directly measured and model-predicted using models \u0026mdash; was evaluated using three complementary metrics. First, the within-subject standard deviation (Sw) quantified the variability between repeated measurements for each participant. Second, the coefficient of repeatability (CR) was calculated as 2.77 \u0026times; Sw, with its 95% CI estimated from the chi-squared distribution. This coefficient represents the smallest detectable change that can be interpreted as exceeding expected measurement noise under identical conditions. Third, the intraclass correlation coefficient (ICC) was computed using a two-way random-effects model for absolute agreement [ICC(2,1)], accounting for variability attributable to both participants and repeated measures. ICC were interpreted as follows: \u0026lt;0.50, poor; 0.50\u0026ndash;0.75, moderate; 0.75\u0026ndash;0.90, good; and \u0026gt;\u0026thinsp;0.90, excellent).\u003csup\u003e25\u003c/sup\u003e This approach is particularly suitable for assessing the reliability of repeated measurements obtained with the same device under consistent testing conditions.\u003c/p\u003e\n\u003cp\u003eDifferences between repeated measurements were analysed using two-tailed paired t-tests. To the inflation of type I error arising from multiple comparisons across the seven predictive models and the measured AL, the significance threshold was adjusted using Bonferroni correction (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.05/8\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e\n\u003ch3\u003eAgreement Analysis\u003c/h3\u003e\n\u003cp\u003eAgreement between measured and model-predicted AL was evaluated under both cycloplegic and non-cycloplegic conditions. For each participant, the reference AL was defined as the mean of two repeated measurements. Predicted AL values were derived using each model\u0026rsquo;s formula, with SE and K\u003csub\u003emean\u003c/sub\u003e from the same measurement condition.\u003c/p\u003e\n\u003cp\u003eAgreement was quantified by the mean difference between measured and predicted AL values, representing systematic bias. The 95% limits of agreement (LoA) were defined as the mean difference\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 times the SD of the paired differences, indicating the interval within which 95% of the differences are expected to lie. Confidence intervals for the LoA were also computed to reflect estimate precision.\u003c/p\u003e\n\u003cp\u003eThe intraclass correlation coefficient [ICC(3,1)] was used to assess relative reliability\u0026mdash;that is, the consistency with which measured and predicted AL values rank across participants, irrespective of systematic bias. The coefficient of variation within subjects (CV\u003csub\u003eWS\u003c/sub\u003e) expressed as a percentage, was calculated as the within-subject SD divided by the mean AL. Together, these metrics provided a scale-independent assessment of prediction reliability.\u003c/p\u003e\n\u003cp\u003ePaired \u003cem\u003et\u003c/em\u003e-tests were used to detect systematic bias between predicted and measured AL. Significance was again adjusted using the Bonferroni correction (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.05/7\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eInfluence of Input Variation on Model-Predicted AL\u003c/h2\u003e\n \u003cp\u003eThe effect of variability in pre-cycloplegic inputs on predicted AL was examined using multiple linear regression, with post-cycloplegic measurements serving as the reference standard. The dependent variable was the difference between pre- and post-cycloplegic predicted AL (\u0026Delta;AL). Independent variables included changes in SE (\u0026Delta;SE) and mean corneal curvature (\u0026Delta;Kmean), while static factors (e.g., age, gender) were excluded. These \u0026Delta;AL were then regressed against the corresponding differences in SE (\u0026Delta;SE) and K\u003csub\u003emean\u003c/sub\u003e (\u0026Delta;K\u003csub\u003emean\u003c/sub\u003e), allowing to determine the extent to which variability in each parameter contributed to deviations in model-predicted AL relative to its cycloplegic baseline.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStatistical Power Calculation\u003c/h3\u003e\n\u003cp\u003eThe study had 88% power to detect a mean difference smaller than 0.2 mm between measured and predicted AL, assuming a standard deviation of 0.5 mm and a Bonferroni-adjusted significance level of 0.007 (0.05/7). A difference of 0.2 mm corresponds to approximately 0.50D, which is considered clinically relevant in paediatric myopia management.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe group comprised 35 myopes, SE\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24 D (range: \u0026minus;5.64 to \u0026minus;\u0026thinsp;0.79 D), 30 emmetropes, SE\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48 D (range: \u0026minus;0.66 to +\u0026thinsp;0.95 D), and 31 hyperopes, SE\u0026thinsp;=\u0026thinsp;+\u0026thinsp;2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56 D (range: +1.00 to +\u0026thinsp;7.25 D), Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eComparison of pre- and post-cycloplegic data showed statistically significant SE shifts toward more positive refractions in emmetropes, hyperopes, and the combined group. Mean differences were \u0026minus;\u0026thinsp;0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 D (95% CI: \u0026minus;0.83 to \u0026minus;\u0026thinsp;0.34, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for emmetropes, \u0026minus;\u0026thinsp;1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39 D (95% CI: \u0026minus;2.26 to \u0026minus;\u0026thinsp;0.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for hyperopes, and +\u0026thinsp;0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 D (95% CI: 0.16 to 1.42, p\u0026thinsp;=\u0026thinsp;0.014) for the overall sample. Myopes showed no significant change (MD\u0026thinsp;=\u0026thinsp;0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30 D; 95% CI: \u0026minus;0.98 to 0.22; p\u0026thinsp;=\u0026thinsp;0.204). Anterior corneal curvature and axial length differences were not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.203).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic, refractive and biometric data pre- and post-cycloplegia, represented by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and range.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003ePre-cycloplegia*\u003c/p\u003e\u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003cp\u003e(range)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003ePost-cycloplegia*\u003c/p\u003e\u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003cp\u003e(range)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE(D)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eK\u003csub\u003emean\u003c/sub\u003e(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAL (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSE(D)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eK\u003csub\u003emean\u003c/sub\u003e(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAL(mm)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyopes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e\u003cp\u003e(-6.27 to -0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003cp\u003e(7.20 to 8.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\u003cp\u003e(22.05 to 26.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\u003cp\u003e(-5.64 to -0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003cp\u003e(7.19 to 8.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\u003cp\u003e(22.05 to 26.70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmmetropes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-1.36 to 0.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\u003cp\u003e(7.31 to 8.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003cp\u003e(22.26 to 24.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-0.42 to 0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\u003cp\u003e(7.31 to 8.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003cp\u003e(22.27 to 24.46)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperopes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-1.54 to 5.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\u003cp\u003e(7.11 to 8.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\u003cp\u003e(20.43 to 23.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.00 to 7.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\u003cp\u003e(7.11 to 8.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87 (20.41 to 24.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-6.27 to 5.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003cp\u003e(7.11 to 8.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e\u003cp\u003e(20.43 to 26.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-5.64 to 7.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\u003cp\u003e(7.11 to 8.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003cp\u003e(20.41 to 26.70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e* - Average of two measurements; M \u0026ndash; Mean; SD \u0026ndash; Standard deviation; SE \u0026ndash; Spherical equivalent; K\u003csub\u003emean\u003c/sub\u003e \u0026ndash; Mean anterior corneal curvature; AL \u0026ndash; Axial length. \u003csup\u003e\u0026dagger;\u003c/sup\u003e - Paired t-test; p\u0026thinsp;=\u0026thinsp;0.014; \u003csup\u003e\u0026Dagger;\u003c/sup\u003e - Paired t-test; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eIntrasession repeatability of measured and predicted AL\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the MD between consecutive AL measurements and predicted AL derived from consecutive SE and K\u003csub\u003emean\u003c/sub\u003e was close to zero, pre- and post-cycloplegia. Measured AL demonstrated the lowest CR pre-cycloplegia (0.14 mm, 95% CI: 0.13 to 0.17mm) and post-cycloplegia (0.09 mm, 95% CI: 0.08 to 0.11mm), outperforming all AL predictive models. Among the models, Tang et al. achieved the best repeatability pre-cycloplegia (0.24 mm, 95% CI: 0.21 to 0.28mm) and post-cycloplegia (0.16 mm, 95% CI: 0.14 to 0.18), whereas He et al. showed the poorest pre-cycloplegia (0.48 mm, 95% CI: 0.42 to 0.56 mm) and post-cycloplegia (0.27 mm, 95% CI: 0.24 to 0.32mm). Overall, both measured and predicted AL improved in CR repeatability after cycloplegia. Intraclass correlation for measured and predicted AL were excellent under both conditions (ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.99, with a range of the 95%LoA of 0.98 to 1.00).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIntrasession repeatability of measured and predicted AL.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003ePre-Cycloplegia\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeasure 1\u003c/p\u003e\u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMeasure 2\u003c/p\u003e\u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMD\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mm)\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCR (mm)\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasured AL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 *\u003c/p\u003e\u003cp\u003e(-0.02 to 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003cp\u003e(0.13 to 0.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHe et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 *\u003c/p\u003e\u003cp\u003e(-0.06 to 0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003cp\u003e(0.42 to 0.56)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKim et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 *\u003c/p\u003e\u003cp\u003e(-0.04 to 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003cp\u003e(0.25 to 0.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTang et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 *\u003c/p\u003e\u003cp\u003e(-0.03 to 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003cp\u003e(0.21 to 0.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMorgan et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13 *\u003c/p\u003e\u003cp\u003e(-0.03 to 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003cp\u003e(0.22 to 0.29)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQueir\u0026oacute;s et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 *\u003c/p\u003e\u003cp\u003e(-0.04 to 0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003cp\u003e(0.26 to 0.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDutt et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 *\u003c/p\u003e\u003cp\u003e(-0.03 to 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003cp\u003e(0.24 to 0.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLingham et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 *\u003c/p\u003e\u003cp\u003e(-0.04 to 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003cp\u003e(0.25 to 0.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePost-Cycloplegia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMethod\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMeasure 1\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eMeasure 2\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eMD\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mm)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eCR (mm)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasured AL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 *\u003c/p\u003e\u003cp\u003e(-0.01 to 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003cp\u003e(0.08 to 0.11)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHe et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 *\u003c/p\u003e\u003cp\u003e(-0.04 to 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003cp\u003e(0.24 to 0.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKim et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 *\u003c/p\u003e\u003cp\u003e(-0.03 to 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003cp\u003e(0.17 to 0.23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTang et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 *\u003c/p\u003e\u003cp\u003e(-0.02 to 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003cp\u003e(0.14 to 0.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMorgan et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 *\u003c/p\u003e\u003cp\u003e(-0.02 to 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003cp\u003e(0.14 to 0.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQueir\u0026oacute;s et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 *\u003c/p\u003e\u003cp\u003e(-0.02 to 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003cp\u003e(0.160 to 0.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDutt et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 *\u003c/p\u003e\u003cp\u003e(-0.02 to 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003cp\u003e(0.15 to 0.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLingham et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 *\u003c/p\u003e\u003cp\u003e(-0.02 to 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003cp\u003e(0.15 to 0.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e*p\u0026thinsp;\u0026gt;\u0026thinsp;0.492 for all; M \u0026ndash;mean; SD \u0026ndash; standard deviation; MD \u0026ndash; mean difference; CR \u0026ndash; coefficient of repeatability; CI \u0026ndash;confidence intervals;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eAgreement between measured and predicted AL\u003c/h2\u003e\u003cp\u003ePre-cycloplegia, all predictive models significantly overpredicted measured AL (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The largest bias was observed with the Kim et al. model (MD=-0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60 mm, 95% CI: -0.99 to \u0026minus;\u0026thinsp;0.75 mm), and the smallest with Morgan et al. predictive model (MD=-0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55 mm, 95% CI: -0.35 to \u0026minus;\u0026thinsp;0.13 mm). Kim et al. also showed the widest LoA (-2.05 to +\u0026thinsp;0.30 mm) and the highest CV\u003csub\u003ews\u003c/sub\u003e (3.13%), whereas Queir\u0026oacute;s et al. exhibited the narrowest LoA (-1.31 to -0.74 mm) and the lowest CV\u003csub\u003ews\u003c/sub\u003e (1.78%). Intraclass correlations indicated good-to-excellent reliability (all ICC\u0026thinsp;≧\u0026thinsp;0.81).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAgreement between measured and estimated AL.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003ePre-Cycloplegia\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePrediction Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAL Predicted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAL\u003c/p\u003e\u003cp\u003eMeasured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMD\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mm)\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLower LoA (mm)\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eUpper LoA (mm)\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCV\u003csub\u003eWS\u003c/sub\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eICC\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(95%LoA)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mm)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHe et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e23.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78 *\u003c/p\u003e\u003cp\u003e(-0.63 to \u0026minus;\u0026thinsp;0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.99\u003c/p\u003e\u003cp\u003e(-2.26 to -1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003cp\u003e(0.87 to 1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003cp\u003e(0.76 to 0.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKim et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60 *\u003c/p\u003e\u003cp\u003e(-0.99 to \u0026minus;\u0026thinsp;0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.05\u003c/p\u003e\u003cp\u003e(-2.25 to \u0026minus;\u0026thinsp;1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003cp\u003e(0.16 to 0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003cp\u003e(-0.12 to 0.94)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTang et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55 *\u003c/p\u003e\u003cp\u003e(-0.81 to \u0026minus;\u0026thinsp;0.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.78\u003c/p\u003e\u003cp\u003e(-1.96 to-1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003cp\u003e(0.25 to 0.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003cp\u003e(0.02 to 0.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMorgan et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55 *\u003c/p\u003e\u003cp\u003e(-0.35 to \u0026minus;\u0026thinsp;0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.33\u003c/p\u003e\u003cp\u003e(-1.50 to \u0026minus;\u0026thinsp;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003cp\u003e(0.71 to 1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003cp\u003e(0.85 to 0.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQueir\u0026oacute;s et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 *\u003c/p\u003e\u003cp\u003e(-0.39 to-0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.31\u003c/p\u003e\u003cp\u003e(-1.47 to \u0026minus;\u0026thinsp;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003cp\u003e(0.62 to 0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003cp\u003e(0.85 to 0.96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDutt et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55 *\u003c/p\u003e\u003cp\u003e(-0.67 to \u0026minus;\u0026thinsp;0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.63\u003c/p\u003e\u003cp\u003e(-1.82 to \u0026minus;\u0026thinsp;1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003cp\u003e(0.39 to 0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003cp\u003e(0.34 to 0.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLingham et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 *\u003c/p\u003e\u003cp\u003e(-0.57 to \u0026minus;\u0026thinsp;0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.49\u003c/p\u003e\u003cp\u003e(-1.65 to \u0026minus;\u0026thinsp;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003cp\u003e(0.44 to 0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003cp\u003e(0.59 to 0.96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePost-Cycloplegia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePrediction Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAL Predicted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAL\u003c/p\u003e\u003cp\u003eMeasured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMD\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mm)\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLower LoA (mm)\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eUpper LoA (mm)\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCV\u003csub\u003eWS\u003c/sub\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eICC\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(95%LoA)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eM\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (mm)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHe et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e23.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81**\u003c/p\u003e\u003cp\u003e(-0.06 to +\u0026thinsp;0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.49\u003c/p\u003e\u003cp\u003e(-1.76 to -1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003cp\u003e(1.50 to 1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.93(0.89 to 0.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKim et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43 *\u003c/p\u003e\u003cp\u003e(-0.65 to -0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.40\u003c/p\u003e\u003cp\u003e(-1.55 to -1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003cp\u003e(0.18 to 0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003cp\u003e(0.22 to 0.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTang et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38*\u003c/p\u003e\u003cp\u003e(-0.49 to \u0026minus;\u0026thinsp;0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.15\u003c/p\u003e\u003cp\u003e(-1.28 to \u0026minus;\u0026thinsp;1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003cp\u003e(0.25 to 0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003cp\u003e(0.53 to 0.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMorgan et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40 **\u003c/p\u003e\u003cp\u003e(-0.02 to 0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.72\u003c/p\u003e\u003cp\u003e(-0.86 to \u0026minus;\u0026thinsp;0.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003cp\u003e(0.75 to 0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003cp\u003e(0.95 to 0.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQueir\u0026oacute;s et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36 **\u003c/p\u003e\u003cp\u003e(0.0 to 0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.64\u003c/p\u003e\u003cp\u003e(-0.76 to \u0026minus;\u0026thinsp;0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003cp\u003e(0.69 to 0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003cp\u003e(0.96 to 0.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDutt et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 *\u003c/p\u003e\u003cp\u003e(-0.31 to -0.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.89\u003c/p\u003e\u003cp\u003e(-1.10 to \u0026minus;\u0026thinsp;0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003cp\u003e(0.43 to 0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003cp\u003e(0.91 to 0.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLingham et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35*\u003c/p\u003e\u003cp\u003e(-0.19 to -0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.81\u003c/p\u003e\u003cp\u003e(-0.92 to \u0026minus;\u0026thinsp;0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003cp\u003e(0.48 to 0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003cp\u003e(0.96 to 0,98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e*Statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.007 (Bonferroni-adjusted significance level); ** p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; \u003csup\u003e\u0026sect;\u003c/sup\u003eTwo-way random-effects model. M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD - Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; MD\u0026thinsp;\u0026plusmn;\u0026thinsp;SD - mean difference\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of the differences; LoA \u0026ndash; Limits of Agreement; CV\u003csub\u003eWS\u003c/sub\u003e - coefficient of variation; ICC- Intraclass correlation coefficients; CI \u0026ndash; Confidence Intervals\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePost-cycloplegia, mean differences between predicted and measured AL decreased across all models. Kim et al retained the largest bias (MD = \u0026minus;\u0026thinsp;0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43 mm, 95% CI: \u0026minus;\u0026thinsp;0.65 to \u0026minus;\u0026thinsp;0.47mm), while Morgan et al. showed the smallest (MD\u0026thinsp;=\u0026thinsp;0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40 mm, 95% CI: \u0026minus;\u0026thinsp;0.02 to 0.14 mm). He et al. had the widest LoA (\u0026ndash;1.49 to 1.69 mm) and highest CV\u003csub\u003ews\u003c/sub\u003e (2.47%), while Lingham et al. demonstrated the narrowest LoA (\u0026ndash;0.81 to 0.57 mm) and one of the lowest CV\u003csub\u003ews\u003c/sub\u003e (1.12%). Reliability improved further (ICC\u0026thinsp;\u0026ge;\u0026thinsp;0.92; 95% LoA: \u0026minus;\u0026thinsp;0.12 to 0.94 mm), post-cycloplegia. A consistent proportional bias was found, shorter eyes (hyperopic and emmetropic) were overpredicted, while myopic eyes showed near-accurate estimates, except in the He et al. model, which displayed the opposite pattern.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGrouped AL analysis confirmed that cycloplegia reduced overestimation in hyperopic and emmetropic eyes, while myopes tended to maintain the underestimation, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The proportion of eyes with prediction errors\u0026thinsp;\u0026lt;\u0026thinsp;0.5 mm, increased from 23% to 43% for Kim et al. and from 66% to 83% for Queir\u0026oacute;s et al.; post-cycloplegia, the Tang, Morgan, Queir\u0026oacute;s, and Lingham models predicted AL within \u0026plusmn;\u0026thinsp;1.00 mm in \u0026gt;\u0026thinsp;97% of eyes, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eInfluence of SE and K\u003csub\u003emean\u003c/sub\u003e Cycloplegia Variation on Model-Predicted AL\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the relationship between cycloplegia-induced changes in spherical equivalent (ΔSE) and mean corneal curvature (ΔK\u003csub\u003emean\u003c/sub\u003e) with variation in predicted axial length (ΔAL). Across all models, ΔSE\u0026mdash; defined as post- minus pre-cycloplegic SE \u0026mdash;was the main driver of ΔAL variation, whereas ΔKmean had minimal impact. Multivariate linear regression (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) confirmed this trend, with all models statistically significant, explaining 98.8\u0026ndash;99.7% of the variance in ΔAL. The contribution of ΔSE accounted for 97.3\u0026ndash;99.1% of the explained variance, while ΔKmean contributed only 0.5\u0026ndash;1.2%. These findings demonstrate that cycloplegia-induced changes in SE are the principal determinant of variation in predicted AL, whereas alterations in corneal curvature play a negligible role.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate linear regression parameters for AL prediction error (ΔAL\u0026thinsp;=\u0026thinsp;AL pre-cycloplegia \u0026ndash; post-cycloplegia)., respectively.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e \u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e \u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eError\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHe et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKim et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTang et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMorgan et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQueir\u0026oacute;s et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDutt et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLingham et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eβ\u003csub\u003e1\u003c/sub\u003e and β\u003csub\u003e2\u003c/sub\u003e are the regression coefficients of ΔSE (pre-cycloplegia \u0026ndash; post-cycloplegia); ΔK\u003csub\u003emean\u003c/sub\u003e (pre-cycloplegia - post-cycloplegia)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study compared the performance of several AL prediction models in a paediatric population, before and after cycloplegia. The models were based on linear regression incorporating ocular (SE and K\u003csub\u003emean\u003c/sub\u003e) and demographic parameters (age and sex), with SE showing a strong association with AL.\u003csup\u003e9\u003c/sup\u003e It was hypothesized that variability in SE measurements could affect model performance. The findings indicate that, across all models, AL prediction accuracy and repeatability improved following cycloplegia, with five of the seven models showing LoA for AL prediction within 1.0 mm of the measured values after cycloplegia.\u003c/p\u003e\u003cp\u003eCycloplegia shifted the refractive error towards more positive values (+\u0026thinsp;0.79 D), with the effect being more pronounced in emmetropes (+\u0026thinsp;0.59 D) and hyperopes (+\u0026thinsp;1.47 D).\u003csup\u003e24\u003c/sup\u003e This shift is consistent with values reported by Hu et al. (+\u0026thinsp;0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79 D) in children aged 4\u0026ndash;18 years\u003csup\u003e26\u003c/sup\u003e and large-scale studies in 12-year-olds (+\u0026thinsp;0.84 D).\u003csup\u003e22\u003c/sup\u003e This behaviour confirms the greater accommodative effort of emmetropic and hyperopic eyes during autorefraction.\u003csup\u003e23\u003c/sup\u003e Differences in K\u003csub\u003emean\u003c/sub\u003e and AL remained within instrument repeatability limits, indicating that these anatomical parameters are unaffected by cycloplegia.\u003csup\u003e24,27\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eBefore cycloplegia, all models overpredicted AL, with mean differences ranging from \u0026minus;\u0026thinsp;0.87 mm (Kim et al.)\u003csup\u003e14\u003c/sup\u003e to \u0026minus;\u0026thinsp;0.24 mm (Morgan et al.)\u003csup\u003e13\u003c/sup\u003e, and LoA extending up to 1.18 mm from the mean. In the He et al.\u003csup\u003e15\u003c/sup\u003e model, LoA were even broader, spanning 1.53 mm. After cycloplegia, prediction errors decreased: the Kim et al.\u003csup\u003e14\u003c/sup\u003e model overpredicted by \u0026minus;\u0026thinsp;0.56 mm, whereas the He et al.\u003csup\u003e15\u003c/sup\u003e model slightly underpredicted (+\u0026thinsp;0.10 mm). Models by Morgan et al.\u003csup\u003e13\u003c/sup\u003e and Queir\u0026oacute;s et al.\u003csup\u003e17\u003c/sup\u003e showed mean differences below 0.10 mm, corresponding to refractive errors under 0.25 D according to the Gullstrand model eye. LoA also narrowed (\u0026lt;\u0026thinsp;0.84 mm) for all models except He et al.,\u003csup\u003e15\u003c/sup\u003e whose precision remained similar to pre-cycloplegia conditions. Using the relationship of 0.4 mm axial elongation per 1.0 D,\u003csup\u003e28\u003c/sup\u003e the LoA still represent potential differences of about 2.0 D \u0026mdash;highlighting that AL estimations from predictive models should be interpreted cautiously.\u003c/p\u003e\u003cp\u003eBland-Altman and regression analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) show that most models (except He et al.)\u003csup\u003e15\u003c/sup\u003e tended to overpredict AL in emmetropic and hyperopic eyes and underpredict in myopic eyes. Grouping eyes by AL range (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed that prediction errors were greatest in shorter eyes (hyperopic and emmetropic) and reduced post-cycloplegia. Myopic eyes showed smaller, more stable errors. Post-cycloplegia, especially for the Queir\u0026oacute;s et al. and Lingham et al. models, 50% of eyes with AL between 21\u0026ndash;26 mm clustered around the zero-error line, with 95% of cases within 1.0 mm. The reduction in proportional bias post-cycloplegia observed in the Bland\u0026ndash;Altman analysis appears particularly relevant in eyes with more extreme axial lengths.\u003c/p\u003e\u003cp\u003eThe performance of a prediction model depends on its developmental methodology, the population used, and the independent variables selected. These factors influence the model\u0026rsquo;s generalizability. All reviewed models share a strong dependence of AL on SE and anterior corneal curvature, grounded in the principles of ocular refraction. He, Tang, Queir\u0026oacute;s and Lingham et al.\u003csup\u003e15\u0026ndash;17,20\u003c/sup\u003e further incorporated age and sex, which improved model fit and are known to influence AL.\u003c/p\u003e\u003cp\u003eHe et al. demonstrated that ~\u0026thinsp;83% of SE variance is explained by the AL/ K\u003csub\u003emean\u003c/sub\u003e ratio.\u003csup\u003e15\u003c/sup\u003e They developed an SE prediction model based on AL, K\u003csub\u003emean\u003c/sub\u003e, and sex, later reformulated here to predict AL. Their sample comprised 3922 Asian children aged 6\u0026ndash;12 years-old with cycloplegic refraction. The model showed underprediction in hyperopes and overprediction in myopes\u0026mdash;opposite to other models\u0026mdash;likely because their SE model estimated more positive SE in hyperopes and more negative SE in myopes. Sex-related differences in AL (males: +0.44 mm) and Kmean (females: +0.13 mm) were consistent with our data (AL: +0.70 mm; K\u003csub\u003emean\u003c/sub\u003e: +0.19 mm, data not shown), suggesting that including sex can enhance performance.\u003c/p\u003e\u003cp\u003eKim et al. derived a model from the simplified Gullstrand eye, using Kmean and SE with a correction constant for SE variation.\u003csup\u003e14\u003c/sup\u003e Their 382-participant Asian cohort (ages 7\u0026ndash;77 years) was measured without cycloplegia. They reported an AL overprediction of 0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 mm (95% LoA: \u0026minus;\u0026thinsp;0.75 to +\u0026thinsp;1.10 mm), with myopes showing the largest errors. In our cohort, pre-cycloplegia overprediction was higher (0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60 mm; 95% LoA: \u0026minus;\u0026thinsp;2.05 to +\u0026thinsp;0.30 mm), especially in hyperopic and emmetropic eyes. Kim et al. reported 75.5% and 95.5% of predictions within 0.5 mm and 1.0 mm, respectively\u0026mdash;substantially higher than our findings (23% \u0026le; 0.5 mm; 61% \u0026le; 1.0 mm). These differences likely reflect cohort characteristics, since age and high ametropia reduce model precision.\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTang et al. developed an AL prediction model using linear regression and machine learning on 1011 myopic Asian children aged 6\u0026ndash;18 years-old, with cycloplegic refraction. Their predictors (K\u003csub\u003emean\u003c/sub\u003e, SE, sex, and age) explained 81% of AL variance\u0026mdash;comparable to 82% (pre-) and 92% (post-cycloplegia) in this study. The inclusion of age accounted for AL elongation during childhood and adolescence and the concurrent reduction in lens power.\u003csup\u003e29\u0026ndash;31\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eMorgan et al. proposed a linear regression model based on cycloplegic SE and K\u003csub\u003emean\u003c/sub\u003e in 144 Caucasian participants aged 8\u0026ndash;12 years-old, later validated in 1046 individuals aged 6\u0026ndash;22 years.\u003csup\u003e13\u003c/sup\u003e They reported an AL underestimation of 0.13 mm (95% LoA: \u0026minus;\u0026thinsp;0.73 to +\u0026thinsp;0.99 mm), consistent with our post-cycloplegia results (0.06 mm; 95% LoA: \u0026minus;\u0026thinsp;0.72 to +\u0026thinsp;0.84 mm). Queir\u0026oacute;s et al. applied this model to 1783 participants aged 6\u0026ndash;25 years-old (SE measured with an open-field autorefractor) and found an AL overestimation of 0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48 mm (95% LoA: +0.70 to +\u0026thinsp;1.20 mm), aligning with our pre-cycloplegia data.\u003csup\u003e17\u003c/sup\u003e Queir\u0026oacute;s et al proposed a new model, adding age as a predictor, which explained\u0026thinsp;~\u0026thinsp;80% of AL variability. In the preset study, this model showed an 0.28 mm overprediction (one of the lowest) pre-cycloplegia and an 0.08 mm underprediction after cycloplegia\u0026mdash;supporting the role of accommodation control in improving AL prediction.\u003c/p\u003e\u003cp\u003eDutt et al. developed a regression model using cycloplegic SE and K\u003csub\u003emean\u003c/sub\u003e from 1301 Caucasian adults aged 18\u0026ndash;22 years-old.\u003csup\u003e18\u003c/sup\u003e Under non-cycloplegic and cycloplegic conditions, their model overestimated AL by 0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 mm (95% LoA: \u0026minus;\u0026thinsp;0.92 to +\u0026thinsp;0.11 mm) and 0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49 mm (95% LoA: \u0026minus;\u0026thinsp;0.94 to +\u0026thinsp;0.97 mm), respectively. In our cohort, overestimations were greater (pre-cycloplegia: \u0026minus;\u0026thinsp;0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55 mm; post-cycloplegia: \u0026minus;\u0026thinsp;0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 mm), reaffirming that cycloplegia enhances precision and accuracy.\u003c/p\u003e\u003cp\u003eLingham et al. proposed a regression model based on cycloplegic SE, K\u003csub\u003emean\u003c/sub\u003e, sex, and age, trained on 1068 Caucasians (6\u0026ndash;20 years-old), 3429 Asians (5\u0026ndash;18 years-old), and 240 Caucasian myopes (6\u0026ndash;19 years-old). Their model underpredicted AL by 0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40 mm (95% LoA: \u0026minus;\u0026thinsp;0.71 to +\u0026thinsp;0.87 mm) in a myopic test set. In our cohort after cycloplegia, it slightly overpredicted AL (\u0026ndash;0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35 mm; 95% LoA: \u0026minus;\u0026thinsp;0.81 to +\u0026thinsp;0.57 mm), likely due to inclusion of hyperopic and emmetropic eyes.\u003c/p\u003e\u003cp\u003eRegression analysis of AL prediction errors against cycloplegic variations in Kmean and SE showed that changes in spherical equivalent (ΔSE) explained 97.3%\u0026ndash;99.1% of the variance in predicted AL across all models, whereas mean corneal curvature (ΔKmean) contributed minimally (0.5%\u0026ndash;1.2%). Considering the reported repeatability of approximately 0.65 D for non-cycloplegic objective refraction, 0.32 D after cycloplegia,\u003csup\u003e24\u003c/sup\u003e and 0.35 D for subjective refraction,\u003csup\u003e32\u003c/sup\u003e the resulting prediction error may reach\u0026thinsp;~\u0026thinsp;0.15 mm, limiting the detection of subtle AL changes. These findings confirm that precise SE measurement is the primary determinant of axial length estimation accuracy and repeatability.\u003c/p\u003e\u003cp\u003eA major strength of this study lies in the direct comparison of multiple AL prediction models on the same paediatric cohort, both before and after cycloplegia. Since these models rely on routinely acquired clinical parameters, the findings offer practical insights into their clinical applicability. The inclusion of a balanced distribution of refractive error types allows for a broader and more representative comparison than those of Tang et al. and Lingham et al., which were limited to myopic eyes. In the context of myopia progression, monitoring AL in emmetropic and low-hyperopic eyes is clinically relevant, as a reduction in hyperopia may indicate axial elongation.\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eHowever, some limitations should be acknowledged. The sample size was smaller than those in the original model development studies. Nonetheless, this study aimed to compare rather than validate or train new models, for which the sample size was adequate. Additionally, only paediatric Caucasian participants were included, whereas most existing models were derived from Asian or mixed cohorts. Although ethnic differences in AL have been,\u003csup\u003e33\u003c/sup\u003e they appear to stem mainly from ocular biometric relationships rather than ethnicity itself,\u003csup\u003e20\u003c/sup\u003e supporting the validity of our comparisons while underscoring the need for broader cross-population assessments.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlights the importance of cycloplegic refraction for improving the accuracy and repeatability of AL predictive models. The models of Morgan et al.,\u003csup\u003e13\u003c/sup\u003e Queir\u0026oacute;s et al.,\u003csup\u003e17\u003c/sup\u003e and Lingham et al.\u003csup\u003e20\u003c/sup\u003e demonstrated minimal bias and superior repeatability under cycloplegic conditions. SE was the primary factor influencing prediction errors, while corneal curvature had negligible impact. Overall, AL predictions were more accurate in myopic eyes than in hyperopic or emmetropic eyes, where mild overprediction persisted. These models provide a useful alternative in primary care settings without optical biometers, particularly for paediatric myopia management, though they should not replace direct AL measurements when available.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI. S. and P.S. performed the conceptualization, formal analysis, methodology, software, supervision, prepared figures and wrote the main manuscript text. A.B. performed formal analysis, supervision, methodology and wrote the main manuscript text. O.T. performed the conceptualization, data curation and validation. All authors reviewed the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors want to thank Agustin Pe\u0026ntilde;aranda for his role in the clinical data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was carried out in accordance with the principles of the Declaration of Helsinki, and was approved by the local ethics committee (Comit\u0026eacute; \u0026Eacute;tico para Investigaci\u0026oacute;n Cl\u0026iacute;nica de Badajoz).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA written informed consent for each participant was obtained from parents or legal guardians.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen access funding provided by FCT|FCCN (b-on). The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the author(s)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIvo Soares http://orcid.org/0000-0001-6712-7514\u003c/p\u003e\n\u003cp\u003eAnt\u0026oacute;nio Baptista https://orcid.org/0000-0002-7304-8756\u003c/p\u003e\n\u003cp\u003eOscar Torrado https://orcid.org/0000-0001-5808-7602\u003c/p\u003e\n\u003cp\u003ePedro Serra https://orcid.org/0000-0003-0471-0213\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e1. Gajjar S, Ostrin LA. 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Regional/ethnic differences in ocular axial elongation and refractive error progression in myopic and non-myopic children. \u003cem\u003eOphthalmic and Physiological Optics\u003c/em\u003e. 2025;45(1):135\u0026ndash;151. doi:10.1111/opo.13401\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"ophthalmic-and-physiological-optics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Ophthalmic and Physiological Optics](https://link.springer.com/journal/44402)","snPcode":"44402","submissionUrl":"https://submission.springernature.com/new-submission/44402/3?","title":"Ophthalmic and Physiological Optics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7974559/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7974559/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eClinical Relevance\u003c/h2\u003e\u003cp\u003eAccurate axial length (AL) estimation is vital for monitoring myopia progression in children, especially in primary care where optical biometers are often unavailable. Prediction models adjusted for cycloplegic effects may offer a reliable alternative.\u003c/p\u003e\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eTo assess the effect of cycloplegia on the accuracy and repeatability of several AL prediction models in a paediatric cohort, and to identify which models maintain minimal bias under both cycloplegic and non-cycloplegic conditions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eNinety-six children (mean age 12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 years-old) underwent repeated measurements pre- and post-cycloplegia of spherical equivalent (SE), anterior corneal curvature (Kmean), and AL using the Myopia Master. Seven published prediction models incorporating SE, Kmean, age, and sex were evaluated. Agreement, bias, limits of agreement (LoA), coefficient of repeatability (CR), intraclass correlation coefficient (ICC), and regression analyses were used to assess performance and repeatability.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCycloplegia induced a hyperopic shift (mean\u0026thinsp;+\u0026thinsp;0.79 D), most pronounced in emmetropic and hyperopic eyes. Measured AL and all models showed improved repeatability post-cycloplegia (measured AL CR decreased from ~\u0026thinsp;0.14 mm to ~\u0026thinsp;0.09 mm; ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.99). Pre-cycloplegia, models overestimated AL (mean differences MD from \u0026minus;\u0026thinsp;0.87 to \u0026minus;\u0026thinsp;0.24 mm); these biases reduced post-cycloplegia (MD from \u0026minus;\u0026thinsp;0.56 to +\u0026thinsp;0.10 mm). Models by Morgan, Queir\u0026oacute;s, and Lingham had the smallest bias (\u0026lt;\u0026thinsp;0.10 mm) and narrowest LoA (\u0026lt;\u0026thinsp;0.84 mm). Variation in SE accounted for ~\u0026thinsp;97\u0026ndash;99% of change in predicted AL; Kmean contributed\u0026thinsp;\u0026le;\u0026thinsp;1.2%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eCycloplegic refraction significantly enhances both accuracy and repeatability of AL prediction models in children. Models by Morgan et al., Queir\u0026oacute;s et al., and Lingham et al. performed best. Predictive models may be a valuable substitute in settings without access to optical biometers, provided cycloplegic measurements are used when possible.\u003c/p\u003e","manuscriptTitle":"Influence of cycloplegia on the axial length prediction models in a peadiatric cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 08:33:28","doi":"10.21203/rs.3.rs-7974559/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-12T20:09:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-12T19:25:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319397422902310594271804627908968124673","date":"2026-01-05T09:16:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-25T22:48:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265011380037049686476861387913597304375","date":"2025-11-05T17:07:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-31T16:58:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-31T16:55:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-30T23:16:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Ophthalmic and Physiological Optics","date":"2025-10-29T01:55:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ophthalmic-and-physiological-optics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Ophthalmic and Physiological Optics](https://link.springer.com/journal/44402)","snPcode":"44402","submissionUrl":"https://submission.springernature.com/new-submission/44402/3?","title":"Ophthalmic and Physiological Optics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5884cd40-f6b3-4f1e-8317-d94927766d05","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T14:10:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 08:33:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7974559","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7974559","identity":"rs-7974559","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}