Pelvic Incidence is Not Fixed: Postoperative Changes in Thoracolumbar Scheuermann’s Kyphosis Compared With Thoracic Type

Pelvic Incidence is Not Fixed: Postoperative Changes in Thoracolumbar Scheuermann’s Kyphosis Compared With Thoracic Type | 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 Pelvic Incidence is Not Fixed: Postoperative Changes in Thoracolumbar Scheuermann’s Kyphosis Compared With Thoracic Type Muhammed Fatih Serttaş, Onur Ortahisar, Fevzi SAĞLAM, Alauddin Kochai, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7889500/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Pelvic incidence(PI) has traditionally been regarded as a fixed anatomical parameter. However, emerging evidence suggests that PI may change following spinal deformity correction. Comparative data on postoperative spinopelvic adaptations in thoracic(TSK) versus thoracolumbar(TLSK) Scheuermann kyphosis, and their deviation from healthy populations, remain limited. This study aimed to compare preoperative spinopelvic and sagittal alignment parameters between TSK and TLSK, evaluate their differences relative to healthy controls, and investigate postoperative changes and interrelationships among these parameters. Methods Fifty-two patients with Scheuermann kyphosis(30 TSK, 22 TLSK; mean follow-up 4.6 ± 2.2 years) were retrospectively analyzed and compared with 30 age-matched healthy controls. Radiographic parameters included thoracic kyphosis(TK), thoracolumbar kyphosis(TLK), lumbar lordosis(LL), cervical lordosis(CL), pelvic incidence(PI), sacral slope(SS), pelvic tilt(PT), sagittal vertical axis(SVA), and PI–LL mismatch. Measurements were obtained preoperatively, postoperatively, and at final follow-up. Group comparisons, correlation analyses, and multivariate regression were performed to identify predictors of PI. Results TLSK patients demonstrated significantly lower preoperative PI and SS compared with TSK and controls(p < 0.05). Both SK groups exhibited greater TK, TLK, LL, and CL compared with controls, while PT and SVA were similar. Following surgery, TLSK patients showed a significant increase in PI(p = 0.011) and SS, eliminating preoperative differences between groups. Preoperative disparities in SVA, TK, and TLK resolved by final follow-up, whereas CL remained significantly lower in TLSK(p = 0.012). Regression analysis identified LL, PI–LL mismatch, PT, and SS as significant predictors of PI(R²=0.945), with LL exerting the strongest influence. Conclusions Surgical correction achieves restoration of global sagittal balance in both TSK and TLSK. PI, long considered immutable, may increase postoperatively in TLSK, highlighting its dynamic behavior. Comprehensive preoperative assessment of sagittal alignment—particularly PI and LL—is crucial for optimal surgical planning in Scheuermann kyphosis. Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Scheuermann’s kyphosis (SK) is classically defined by the presence of at least three contiguous vertebral bodies with ≥ 5° of anterior wedging.[ 1 ] SK has been categorized into typical and atypical forms [ 14 ]. The typical variant usually has an apex in the mid-thoracic region (T7–T9) and satisfies Sorensen’s criteria, whereas the atypical variant more frequently presents with an apex at the thoracolumbar or lumbar levels, where classic radiographic findings such as disc space narrowing, endplate irregularities, and Schmorl’s nodes may be present, although Sorensen’s criteria are not always fulfilled [ 2 ]. The balance between the pelvis and spine is essential in determining a person's overall global spinal alignment.[ 15 ] Despite being viewed as a fixed anatomical measurement [ 6 ], pelvic incidence (PI) has been shown to have the potential for change in recent studies. [3,1,4໿]. However, these findings are mostly reported in adult deformity cohorts, and postoperative PI behavior in adolescent Scheuermann’s kyphosis remains unclear [ 10 , 12 , 17 ]. The literature has not clearly compared the effects of thoracic (TSK) versus thoracolumbar (TLSK) variants on PI, and the role of fusion extending into the lower lumbar segments is still uncertain [ 12 , 16 , 17 ]. This study therefore aims to address this gap by evaluating PI variability and its relationship with other spinopelvic parameters in SK subtypes. MATERIAL AND METHOD Patient Selection and Clinical Investigation Following ethics committee approval, 102 patients with Scheuermann kyphosis who underwent single-stage posterior pedicle screw fusion between January 2015 and March 2021 were retrospectively reviewed. Inclusion criteria were: 1) patients diagnosed with SK under the age of 20 according to Sorensen criteria [ 1 ]; 2) visibility of the pelvis, femoral heads, and C2 vertebral body on standing whole spine AP and lateral radiographs in the preoperative and postoperative periods; 3) follow-up period of at least two years, ; Exclusion criteria were: patients with postural kyphosis, congenital spinal deformity, neuromuscular disease, patients with previous spine surgery or anterior spine surgery and patients who undergo revision spine surgery. According to the aforementioned criteria, 52 patients with Scheuermann kyphosis were included in the study.Thirty healthy participants of similar age were chosen from our database to form the control group. Patients were divided into two groups according to the kyphotic apex vertebra level: Thoracic Scheuermann Kyphosis (TSK) and Thoracolumbar Scheuermann Kyphosis (TLK) Thoracic kyphosis (TSK) group, apex vertebra T9 or higher; The thoracolumbar kyphosis (TLSK) group was divided into those with apex vertebra T10 or below. [ 7 ]. All operated patients had a preoperative global kyphosis angle greater than 70°.The lowest instrumented vertebra was selected as the sagittal stable vertebra (SSA) or SSA-1, while the upper instrumented vertebra (UIV) was chosen as the proximal end vertebra (PEV) of the kyphotic deformity. Accordingly, 30 patients were included in the TSK group, 22 in the TLSK group, and 30 in the control group. Radiological Evaluation Preoperative and final follow-up radiographs of the patients were taken on long cassettes in the posteroanterior and lateral planes while standing using standard procedures. Radiological measurements were evaluated using the SURGİMAP (Nemaric Inc., USA) ( https://www.surgimap.com/ ) measurement system, and all measurements were meticulously performed by two independent spine surgeons.Nine spinopelvic parameters [ 8 ] on standing lateral view radiograms were evaluated at the preoperative and final follow-up assessments: 1) thoracic kyphosis (TK, T5–T12); 2) thoracolumbar kyphosis (TLK, T10–L2); 3) cervical lordosis (CL, C2–C7 Cobb angle); 4) lumbar lordosis (LL, L1–S1); 5) pelvic incidence (PI); 6) sacral slope (SS); 7) pelvic tilt (PT); 8) C7 sagittal vertical axis (SVA); 9 )PI-LL mismatch. ( Fig. 1 ) All data were given the mean standard error of three separate measurements, and categorical variables were reported as percentages. To limit interobserver variability, radiographs were assessed separately by two authors of the current study. An experienced spine surgeon also reviewed all medical records and radiographs in each instance. STATISTICAL ANALYSIS All data were analyzed using the NCSS (Number Cruncher Statistical System) 2020 Statistical Software (NCSS LLC, Kaysville, Utah, USA) ( Fig. 2 , 3 ) Quantitative variables were presented as mean, standard deviation, median, minimum, and maximum values, while qualitative variables were expressed as frequencies and percentages. The Shapiro–Wilk test and Box plot graphics were used to assess the normality of distribution.For comparisons between two groups, the Student’s t-test was applied to normally distributed variables, whereas the Mann–Whitney U test was used for non-normally distributed variables. Relationships between continuous variables were evaluated with the Pearson correlation test. For paired measurements, the Paired Samples t-test was used in normally distributed data. Qualitative data were compared using the Chi-square test or the Fisher’s Freeman–Halton test where appropriate. A multivariate linear regression analysis was performed to determine the effects of spinopelvic parameters on pelvic incidence (PI). Results were interpreted at a 95% confidence interval, and a p-value < 0.05 was considered statistically significant. RESULTS The mean follow-up duration of the patients included in the study was 4.6 ± 2.2 years, and the mean fusion segment length was 11.4 ± 0.9; no significant difference was detected between the groups (p < 0.05). Demographic data are shown in Table 1. No significant differences were found between groups (p = 0.41).When the apex vertebrae of the cases were examined, it was determined that in 30 patients the apex was at T9 or above, and in 22 patients it was at T10 or below (Table 1). Compared with the control group, the preoperative values of TK, TLK, GK, LL, and CL were significantly higher in both TLSK and TSK patients (p 0.05). In TSK patients, no significant differences were found in PI and SS values compared to the control group preoperatively (p > 0.05); however, in TLSK patients, PI and SS values were found to be significantly lower (p < 0.05) (Table 2).Preoperatively, the PI and SS values of the TLSK group were significantly lower compared with the TSK group (p 0.05). In addition, a significant postoperative increase was found in the PI and SS values in the TLSK group (p = 0.011). Among the TLSK and TSK groups, SVA, TK, and TLK values showed significant differences preoperatively; however, in the final follow-up, no significant differences were found between these parameters (p > 0.05). When sagittal radiological measurements were compared between the two groups, only the CL value showed a difference at the final follow-up. While preoperative CL values did not show a significant difference, at the final follow-up the CL value in the TLSK group was found to be significantly lower (p = 0.012) (Table 3).In the TLSK group, a statistically strong positive correlation was observed between ΔPI values and ΔPT values, and between ΔSS values and ΔCL values (r = 0.731, p = 0.001; r = 0.598, p = 0.003, respectively). A statistically moderate negative correlation was found between ΔLL values and ΔCL values (r = − 0.425; p = 0.045) (Table 4).In the univariate analysis of risk factors, preoperative PT, preoperative SS, preoperative LL, preoperative PI–LL, preoperative TLK, preoperative CL, and preoperative SVA measurements were found to be individually effective risk factors on preoperative PI measurement. According to the model summary showing the degrees of effect of the risk factors on preoperative PI measurement (R² = 0.945), the risk factors affected the preoperative PI measurement by 94.5% (Table 5). Table 1: Distributions of Descriptive Characteristics n (%) Gender Female 43 (52,4) Male 39 (47,6) Age Mean ± SD 16,85±1,65 Median (Min–Max) 17 (13-19) Follow-up duration (years) (n=52) Mean ± SD 4,60±2,21 Median (Min–Max) 4 (2-8) Apex vertebra (n=52) T6 2 (3,8) T7 12 (23) T8 14 (26,9) T9 2 (3,8) T10 7 (13,4) T11 11 (21,1) T12 4 (7,6) Length of fused segments (n=52) Mean ± SD 11,44±0,96 Median (Min–Max) 12 (9-13) Group TLSK 22 (26,8) TSK 30 (36,6) Control 30 (36,6) Table 3: Comparison of Radiological Parameters by Groups Group b p TLSK (n=22) TSK (n=30) PI Preoperative Mean ± SD 37,77±10,42 44,87±8,89 0,011* Final follow-up (n=52) Mean ± SD 43,95±12,46 45,83±10,92 0,566 e p 0,011* 0,528 Change (Δ) Mean ± SD 6,18±10,39 0,97±8,29 0,049* PT Preoperative Mean ± SD 11,95±10,05 11,07±7,48 0,716 Final follow-up (n=52) Mean ± SD 13,82±7,89 12,60±8,13 0,591 e p 0,394 0,163 Change (Δ) Mean ± SD 1,86±10,05 1,53±5,87 0,882 SS Preoperative Mean ± SD 25,73±7,29 32,53±9,90 0,009** Final follow-up (n=52) Mean ± SD 30,18±11,18 33,3±8 0,246 e p 0,011* 0,690 Change (Δ) Mean ± SD 4,45±7,44 0,77±10,41 0,163 LL Preoperative Mean ± SD -63,09±14,31 -69,17±10,75 0,086 Final follow-up (n=52) Mean ± SD -51,77±12,33 -50,8±12,37 0,780 e p 0,003** 0,001** Change (Δ) Mean ± SD 11,32±16,07 18,37±9,60 0,054 PI–LL Preoperative Mean ± SD -24,73±17,41 -23,17±11,13 0,695 Final follow-up (n=52) Mean ± SD -8,05±11,54 -5,4±11,23 0,411 e p 0,001** 0,001** Change (Δ) Mean ± SD 16,68±17,39 17,77±10,99 0,784 TLK Preoperative Mean ± SD 44,77±11,53 22,23±12,79 0,001** Final follow-up (n=52) Mean ± SD 12,36±10,07 9,63±9,57 0,325 e p 0,001** 0,001** Change (Δ) Mean ± SD -32,41±16,37 -12,60±13,98 0,001** TK Preoperative Mean ± SD 62,77±11,52 53,57±10,5 0,004** Final follow-up (n=52) Mean ± SD 32,73±7,38 30,17±9,89 0,312 e p 0,001** 0,001** Change (Δ) Mean ± SD -30,05±13,12 -23,40±11,82 0,062 CL Preoperative Mean ± SD -22,18±15,91 -26,20±18,86 0,422 Final follow-up (n=52) Mean ± SD -11,55±15,81 -24,07±17,82 0,012* e p 0,028* 0,550 Change (Δ) Mean ± SD 10,64±21,20 2,13±19,30 0,139 SVA Preoperative Mean ± SD -5,82±43,69 -28,23±25,91 0,025* Final follow-up (n=52) Mean ± SD -18,23±16,61 -23,33±41,16 0,586 e p 0,183 0,525 Change (Δ) Mean ± SD -12,41±42,31 4,90±41,72 0,148 b Student-t Test e Paired Samples t-Test *p<0,05 **p<0,01 Table 4: The Relationship Between Preoperative-Postoperative Radiological Parameters According to TLSK TLSK ∆ PI ∆ PT ∆ SS ∆ LL ∆ PI–LL ∆ TLK ∆ TK ∆ CL ∆ SVA ∆ PI r 1 0,731 0,417 -0,206 0,431 0,030 0,020 0,053 -0,178 p 0,001** 0,054 0,357 0,045* 0,896 0,928 0,816 0,428 ∆ PT r 0,731 1 -0,309 0,152 0,559 0,385 0,348 -0,381 0,143 p 0,001** 0,162 0,499 0,007** 0,077 0,112 0,081 0,527 ∆ SS r 0,417 -0,309 1 -0,521 -0,180 -0,414 -0,413 0,598 -0,449 p 0,054 0,162 0,013* 0,422 0,055 0,056 0,003** 0,036* ∆ LL r -0,206 0,152 -0,521 1 0,778 0,130 -0,176 -0,425 0,431 p 0,357 0,499 0,013* 0,001** 0,565 0,433 0,049* 0,045* ∆ PI–LL r 0,431 0,559 -0,180 0,778 1 0,085 -0,198 -0,325 0,254 p 0,045* 0,007** 0,422 0,001** 0,706 0,378 0,140 0,254 ∆ TLK r 0,030 0,385 -0,414 0,130 0,085 1 0,482 -0,342 0,433 * p 0,896 0,077 0,055 0,565 0,706 0,023* 0,120 0,044* ∆ TK r 0,020 0,348 -0,413 -0,176 -0,198 0,482 1 -0,241 0,372 p 0,928 0,112 0,056 0,433 0,378 0,023* 0,280 0,088 ∆ CL r 0,053 -0,381 0,598 -0,425 -0,325 -0,342 -0,241 1 -0,609 p 0,816 0,081 0,003** 0,049* 0,140 0,120 0,280 0,003** ∆ SVA r -0,178 0,143 -0,449 0,431 0,254 0,433 0,372 -0,609 1 p 0,428 0,527 0,036* 0,045* 0,254 0,044* 0,088 0,003** r:Pearson Correlation Test **p<0,01 *p<0,05 Table 5. Model Summary Model R R Square Corrected R square Std. Error 1 0,972 (a) 0,945 0,941 2,45 As a result of the regression analysis, preoperative PT, preoperative SS, preoperative LL, and preoperative PI–LL measurements were found to have significant effects in the model. Among these, the greatest effect originated from preoperative LL, followed by preoperative PI–LL, preoperative PT, and preoperative SS (Table 6). Regression Model: Preoperative PI = 0.732 + 0.443 (Preop PT) + 0.331 (Preop SS) – 0.618 (Preop LL) + 0.554 (Preop PI–LL) DISCUSSION Pelvic incidence (PI) has traditionally been regarded as a fixed parameter, yet recent studies have challenged this view [ 4 , 5 , 9 , 11 ]. Jiang et al. [ 12 ] emphasized that spinopelvic parameters in Scheuermann’s kyphosis vary according to deformity type. In line with their findings, our study demonstrated that TLSK patients had significantly lower PI and TLK values compared with TSK patients. They attributed these differences to rigid kyphosis and compensatory hyperlordosis, particularly in the lumbar and cervical regions. Jansen et al. [ 16 ] also reported that the rate of LL improvement was greater in TSK than TLSK, which is consistent with our observation of less lumbar hyperlordosis in TLSK cases. Rizkallah et al. [ 10 ] demonstrated that patients with low baseline PI (< 40°) exhibited greater postoperative changes, whereas those with higher PI values showed minimal variation. Consistent with these findings, our TLSK cohort—characterized by a mean preoperative PI of 37.7°—demonstrated a significant postoperative increase. Bederman et al. [ 17 ] further suggested that in patients with low PI values, sagittal balance is maintained through adaptive changes in lumbar lordosis (LL) and pelvic tilt (PT). The postoperative increase in PI and sacral slope (SS) observed in our TLSK group supports this compensatory mechanism. While higher PI values are generally associated with greater compensatory capacity, excessively elevated PI has been linked to degenerative spondylolisthesis [ 18 , 19 ]. This underscores the clinical relevance of understanding PI dynamics in deformity surgery. Similarly, Li et al. [ 13 ] reported lower PI values (mean 35.1°) in patients with congenital or angular kyphosis secondary to tuberculosis, further corroborating our finding that TLSK patients present with inherently lower PI compared with both TSK patients and healthy controls. We observed that both TLSK and TSK patients presented with significantly elevated preoperative TK, TLK, GK, LL, and CL values relative to controls, implying that compensatory adaptations in Scheuermann’s kyphosis primarily occur in the lumbar and cervical regions [ 12 ]. Moreover, PI and SS values in the TLSK group were significantly lower than those in both controls and the TSK group, consistent with the findings of Li et al., who reported kyphotic cases presenting with low PI values [ 13 ]. In addition, the preoperative differences observed in SVA, TK, and TLK became comparable postoperatively, highlighting the effectiveness of surgery in restoring global sagittal balance [ 16 , 21 ]. At the final follow-up, only CL was significantly lower in the TLSK group, suggesting that thoracolumbar correction may limit the adaptive capacity of the cervical spine; similarly, Liu et al. reported a tendency toward decreased cervical lordosis following thoracolumbar kyphosis surgery [ 21 ]. In the TLSK group, strong associations were identified among spinopelvic parameters. Specifically, ∆PI correlated positively with ∆PT, ∆SS correlated positively with ∆CL, while ∆LL correlated negatively with ∆CL. These results indicate that changes in lumbar and pelvic alignment are closely linked to compensatory mechanisms at the cervical level. Our findings are consistent with those of Ye et al. [ 20 ], who reported that increased lumbar lordosis may be accompanied by a compensatory reduction in cervical lordosis. In our study, univariate analysis demonstrated that preoperative PT, SS, LL, PI-LL, TLK, CL, and SVA significantly influenced PI, and the model explained PI with 94.5% accuracy. The strongest effect originated from LL, suggesting that PI should not be regarded as a fixed congenital value but rather as a dynamic parameter shaped within the spinopelvic chain. Similarly, Bederman et al. reported strong associations of LL and PT with PI in patients with Scheuermann’s disease [ 17 ]. Nasto et al. also emphasized PI and LL as critical predictive parameters in preoperative spinopelvic alignment [ 22 ]. This study has certain limitations. The retrospective design inherently carries the risk of selection bias, even though strict inclusion and exclusion criteria were applied. The relatively small sample size may have reduced the statistical power, especially for subgroup comparisons. Radiographic measurements were based on standing lateral radiographs, which do not reflect dynamic or functional changes in spinopelvic parameters during motion or in different positions. Although measurements were performed by two independent observers, the possibility of interobserver variability cannot be entirely excluded. Furthermore, the follow-up period, while sufficient to evaluate short- to mid-term outcomes, may not fully demonstrate the long-term adaptations of pelvic incidence and sagittal balance. These limitations should be considered when interpreting our results, and future prospective, multicenter studies with larger patient populations and longer follow-up are warranted. CONCLUSION This study demonstrates that PI significantly increased after surgery in patients with thoracolumbar Scheuermann kyphosis, whereas no such change was observed in thoracic cases. The extension of fusion into the lower lumbar spine may contribute to this alteration, particularly in TLSK patients with lower baseline PI values. For surgical planning, PI should be assessed together with LL, PT, SS, and the PI–LL mismatch rather than in isolation. Particular attention is required in TLSK patients to maintain sagittal balance, as neglecting PI dynamics may predispose to iatrogenic spondylolisthesis. These findings highlight the importance of comprehensive spinopelvic evaluation in Scheuermann kyphosis surgery. Declarations Author Contribution ●MFS: Data curation, formal analysis, visualization, investigation, resources, project administration, and writing – original draft. Collected, analyzed, and interpreted the data, prepared figures and tables, managed project administration, and drafted the initial manuscript.●OO: Conceptualization, methodology, and writing – original draft. Developed the research concept and study design, and contributed to manuscript drafting.●FS: Collected, analyzed, and interpreted the data, prepared figures and tables, managed project administration, and drafted the initial manuscript.●AK: Provided oversight and leadership, validated findings, and critically reviewed and revised the manuscript.●MEİ: Supervision, validation, and writing – review & editing. References Lonner BS, Toombs CS, Mechlin M, Ciavarra G, Shah SA, Samdani AF, et al. MRI screening in operative Scheuermann kyphosis: is it necessary? Spine Deform. 2017;5(2):124–33. doi:10.1016/j.jspd.2016.10.008 Sardar ZM, Ames RJ, Lenke L. Scheuermann’s kyphosis: diagnosis, management, and selecting fusion levels. J Am Acad Orthop Surg. 2019;27(10):e462–72. doi:10.5435/JAAOS-D-17-00748 Place HM, Hayes AM, Huebner SB, Hayden AM, Israel H, Brechbuhler JL. Pelvic incidence: a fixed value or can you change it? Spine J. 2017;17(10):1565–9. doi:10.1016/j.spinee.2017.06.037 Maekawa A, Endo K, Suzuki H, Sawaji Y, Nishimura H, Matsuoka Y, et al. Impact of pelvic incidence on change in lumbo-pelvic sagittal alignment between sitting and standing positions. Eur Spine J. 2019;28(9):1914–9. doi:10.1007/s00586-019-05891-9 Ohya J, Kawamura N, Takasawa E, Onishi Y, Ohtomo N, Miyahara J, et al. Pelvic incidence change on the operating table. Eur Spine J. 2021;30(9):2473–9. doi:10.1007/s00586-021-06753-z Schwab F, Lafage V, Patel A, Farcy JP. Sagittal plane considerations and the pelvis in the adult patient. Spine (Phila Pa 1976). 2009;34(17):1828–33. doi:10.1097/BRS.0b013e3181a13c08 Lin G, Wang S, Yang Y, Su Z, Du Y, Xu X, et al. The effect of pedicle subtraction osteotomy for the correction of severe Scheuermann thoracolumbar kyphosis on sagittal spinopelvic alignment. BMC Musculoskelet Disord. 2021;22(1):165. doi:10.1186/s12891-020-03942-7 Menezes CM, Lacerda GC, Lamarca S. Sagittal alignment concepts and spinopelvic parameters. Rev Bras Ortop. 2023;58(1):1–8. doi:10.1055/s-0042-1742602 Roussouly P, Gollogly S, Noseda O, Berthonnaud E, Dimnet J. The vertical projection of the sum of the ground reactive forces of a standing patient is not the same as the C7 plumb line: a radiographic study of the sagittal alignment of 153 asymptomatic volunteers. Spine (Phila Pa 1976). 2006;31(11):E320–5. doi:10.1097/01.brs.0000218263.58642.ff Rizkallah M, Shen J, Phan P, Al-Shakfa F, Kamel Y, Liu J, et al. Can pelvic incidence change after lumbo-pelvic fixation for adult spine deformity, and would the change be affected by the type of pelvic fixation? Spine (Phila Pa 1976). 2024;49(1):E1–7. doi:10.1097/BRS.0000000000004651 Schroeder N, Noschenko A, Burger E, Patel V, Cain C, Ou-Yang D, et al. Pelvic incidence changes between flexion and extension. Spine Deform. 2018;6(6):753–61. doi:10.1016/j.jspd.2018.03.008 Jiang L, Qiu Y, Xu L, et al. Sagittal spinopelvic alignment in adolescents associated with Scheuermann’s kyphosis: a comparison with normal population. Eur Spine J. 2014;23(7):1420–6. doi:10.1007/s00586-014-3266-2 Li W, Sun Z, Guo Z, Qi Q, Kim SD, Zeng Y, et al. Analysis of spinopelvic sagittal alignment in patients with thoracic and thoracolumbar angular kyphosis. Spine (Phila Pa 1976). 2013;38(13):E813–8. doi:10.1097/BRS.0b013e3182913219 Blumenthal SL, Roach J, Herring JA. Lumbar Scheuermann’s: a clinical series and classification. Spine (Phila Pa 1976). 1987;12(9):929–32. doi:10.1097/00007632-198712000-00002 Le Huec JC, Thompson W, Mohsinaly Y, Barrey C, Faundez A. Sagittal balance of the spine. Eur Spine J. 2019;28(9):1889–905. doi:10.1007/s00586-019-06083-1 Jansen RC, van Rhijn LW, van Ooij A. Predictable correction of the unfused lumbar lordosis after thoracic correction and fusion in Scheuermann kyphosis. Spine (Phila Pa 1976). 2006;31(11):1227–31. doi:10.1097/01.brs.0000217682.53629.ad Bederman SS, Farhan S, Hu X, Lieberman IH, Belanger TA, Musa A, et al. Sagittal spinal and pelvic parameters in patients with Scheuermann’s disease: a preliminary study. Int J Spine Surg. 2019;13(6):536–43. doi:10.14444/6073 Oh SK, Chung SS, Lee CS. Correlation of pelvic parameters with isthmic spondylolisthesis. Asian Spine J. 2009;3(1):21–6. doi:10.4184/asj.2009.3.1.21 Barrey C, Jund J, Perrin G, Roussouly P. Spinopelvic alignment of patients with degenerative spondylolisthesis. Neurosurgery. 2007;61(5):981–6; discussion 986. doi:10.1227/01.neu.0000303194.02921.30 Ye J, Rider SM, Lafage R, et al. Spinopelvic sagittal compensation in adult cervical deformity. J Neurosurg Spine . 2023;39(1):1-10. Published 2023 Mar 24. doi:10.3171/2023.2.SPINE221295 Liu, C., Zhu, W., Li, Y. et al. How does cervical sagittal profile change after the spontaneous compensation of global sagittal imbalance following one- or two-level lumbar fusion. BMC Musculoskelet Disord 25 , 387 (2024). https://doi.org/10.1186/s12891-024-07518-7 Nasto LA, Jain A, Shalabi A, et al. Correlation between preoperative spinopelvic alignment and risk of proximal junctional kyphosis after posterior-only surgical correction of Scheuermann kyphosis. Spine J. 2016;16(10):1239-1248. DOI: 10.1016/j.spinee.2016.02.042 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7889500","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541748185,"identity":"c0db3102-5300-4a03-86d8-15ffd0358f25","order_by":0,"name":"Muhammed Fatih Serttaş","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDAC5gNQBnsDkDCwIEILWwKQSDBgYOAB6TWQIEWLBIjBQIQW3Tb2h48rf/yRl5/5/OqGHwUSDPzt3Ql4tZgd4zE2PJNgYLjhdk7ZzR6gwyTOnN2AX8v9HjbJhgQDxg3SOWk3eIBaDCRyCWg5xv78J1CL/fyZZ9Ju/iFOC4MZI1BLYsMN9mO3ibSFx1iyIc04ecOZHLbbMgYSPIT9coz94ccGGznb+e3Hn91888dGjr+9F78WJMBjACaJVQ4C7A9IUT0KRsEoGAUjCAAAWyBHpmQ/W+IAAAAASUVORK5CYII=","orcid":"","institution":"Kocaeli City Hospital","correspondingAuthor":true,"prefix":"","firstName":"Muhammed","middleName":"Fatih","lastName":"Serttaş","suffix":""},{"id":541748186,"identity":"34f45975-ca3a-4463-aea8-2954613384c7","order_by":1,"name":"Onur Ortahisar","email":"","orcid":"","institution":"Sakarya University","correspondingAuthor":false,"prefix":"","firstName":"Onur","middleName":"","lastName":"Ortahisar","suffix":""},{"id":541748187,"identity":"920e1b06-0a6b-40b7-b3b6-e37744911446","order_by":2,"name":"Fevzi SAĞLAM","email":"","orcid":"","institution":"Sakarya University","correspondingAuthor":false,"prefix":"","firstName":"Fevzi","middleName":"","lastName":"SAĞLAM","suffix":""},{"id":541748188,"identity":"72c6d6d4-0a69-4fef-940d-d68595ed67ba","order_by":3,"name":"Alauddin Kochai","email":"","orcid":"","institution":"Sakarya University","correspondingAuthor":false,"prefix":"","firstName":"Alauddin","middleName":"","lastName":"Kochai","suffix":""},{"id":541748189,"identity":"b0717408-bebb-4578-b29b-9adbed01c18a","order_by":4,"name":"Mustafa Erkan İnanmaz","email":"","orcid":"","institution":"Sakarya University","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"Erkan","lastName":"İnanmaz","suffix":""}],"badges":[],"createdAt":"2025-10-17 20:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7889500/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7889500/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95807526,"identity":"95b35888-2add-4c6f-8913-f05fcf209c2b","added_by":"auto","created_at":"2025-11-13 08:48:52","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4971471,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptincludeabstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/503a9fdf118b1e8bb544093f.docx"},{"id":95807464,"identity":"f64fe807-0f1b-4f75-92f2-2ee95c15fa68","added_by":"auto","created_at":"2025-11-13 08:48:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15832,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/c9c46464b4eb06c3d7770fd6.docx"},{"id":95807718,"identity":"bd46d8bb-faff-49db-a1fa-499973e51717","added_by":"auto","created_at":"2025-11-13 08:49:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2767165,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/b378256b50dffcaa7683426b.docx"},{"id":95807713,"identity":"2d271d3f-2f9c-485d-a7e6-7ff5f08c8f07","added_by":"auto","created_at":"2025-11-13 08:49:06","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2770324,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/8971d193677e0671ce697c20.docx"},{"id":95807698,"identity":"c748faaa-f1b2-4764-968c-ed131df4e635","added_by":"auto","created_at":"2025-11-13 08:49:05","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2768984,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/d225c6531b13fdfa64cb8b2d.docx"},{"id":95807563,"identity":"412f23a4-9b97-4ddb-8002-21f538a04f55","added_by":"auto","created_at":"2025-11-13 08:48:56","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":140677,"visible":true,"origin":"","legend":"","description":"","filename":"Table5.docx","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/7a3f84bc04e9f1803890d396.docx"},{"id":95807498,"identity":"750d6ba0-9455-4ee3-b1ca-3817dc568cba","added_by":"auto","created_at":"2025-11-13 08:48:47","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141510,"visible":true,"origin":"","legend":"","description":"","filename":"Table6.docx","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/6610becb95ed78a6d556a6aa.docx"},{"id":95807578,"identity":"9617c49e-d58d-4ca6-8f08-3043ad7ad464","added_by":"auto","created_at":"2025-11-13 08:48:57","extension":"json","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7753,"visible":true,"origin":"","legend":"","description":"","filename":"02880fc815b44af981f43f35c807355f.json","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/4651167243a11e2a32420690.json"},{"id":95807397,"identity":"e5ec1bd9-d88b-49ca-b6f7-e423e394ae24","added_by":"auto","created_at":"2025-11-13 08:48:34","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130677,"visible":true,"origin":"","legend":"","description":"","filename":"02880fc815b44af981f43f35c807355f1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/8776c42c539c6075ba31cf99.xml"},{"id":95807556,"identity":"e16f6998-4114-4801-99de-9fa69507f02f","added_by":"auto","created_at":"2025-11-13 08:48:55","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":227725,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/7640ae6bc3482f112c55dea5.jpeg"},{"id":95807527,"identity":"db783b8e-a7df-4461-9125-cd7e3ec8eb11","added_by":"auto","created_at":"2025-11-13 08:48:52","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":643741,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/90af245bdf5278ebbede43c9.jpeg"},{"id":95807618,"identity":"a3383aa3-8e3f-4c33-acbd-4a3ec5640d54","added_by":"auto","created_at":"2025-11-13 08:49:02","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":204041,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/79c3f1e208735ac95ce20d45.jpeg"},{"id":95807511,"identity":"9e016d45-24c2-4010-99ec-0ecede86a403","added_by":"auto","created_at":"2025-11-13 08:48:51","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74693,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/ffcd5ccf50cd59015ec85e4b.png"},{"id":95807403,"identity":"663994c4-1cde-4460-9033-44502bb0ee30","added_by":"auto","created_at":"2025-11-13 08:48:35","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185513,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/e0623688bbc4dc7730fa10a0.png"},{"id":95807519,"identity":"240167f0-8a0a-4b2b-b4b7-8162404dd985","added_by":"auto","created_at":"2025-11-13 08:48:52","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87511,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/973831f15dbe8ca51484c398.png"},{"id":95807540,"identity":"9296065a-bc61-4278-8442-918d83ffa700","added_by":"auto","created_at":"2025-11-13 08:48:54","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127791,"visible":true,"origin":"","legend":"","description":"","filename":"02880fc815b44af981f43f35c807355f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/c46fc4437f72916655f2d267.xml"},{"id":95807598,"identity":"71571a65-2576-42b7-95e3-c59b8b3f67c3","added_by":"auto","created_at":"2025-11-13 08:48:59","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142171,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/cdf06e1de026a40e697a832e.html"},{"id":95807530,"identity":"6690e3f4-4209-4398-896f-a8b97caab04e","added_by":"auto","created_at":"2025-11-13 08:48:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139209,"visible":true,"origin":"","legend":"\u003cp\u003eDemonstrates the measurements of pelvic incidence (PI), pelvic tilt (PT), and sacral slope (SS).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/a7f0b11c97423500c93b1b3c.png"},{"id":95807523,"identity":"22d6c9b2-8db8-4952-ba9a-ae102123e2b6","added_by":"auto","created_at":"2025-11-13 08:48:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":392024,"visible":true,"origin":"","legend":"\u003cp\u003ePre- (A) and postoperative third year (B) radiographs of a 19-year-old female with Thoracic Scheuermann Kyphosis with an apex at T9. Posterior spinal fusion (T3–L1) was achieved with use of pedicle screws and rods system.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/500101de1a85c8bb89de5363.png"},{"id":95807786,"identity":"96117b9b-a528-4a9f-88b9-a829aa33c6aa","added_by":"auto","created_at":"2025-11-13 08:49:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":209392,"visible":true,"origin":"","legend":"\u003cp\u003ePre- (A) and postoperative third year (B) radiographs of a 18-year-old female with Thoracolumbar Scheuermann Kyphosis with an apex at T11-T12. Posterior spinal fusion (T4–L4) was achieved with use of pedicle screws and rods system.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/018a635f29dccccc17cb73c3.png"},{"id":99789480,"identity":"30f6c9b0-2faf-46c4-b36d-fb4a9e616fee","added_by":"auto","created_at":"2026-01-08 12:49:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2230205,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7889500/v1/25a64f70-f523-4ec3-8466-30f1dd0d21be.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePelvic Incidence is Not Fixed: Postoperative Changes in Thoracolumbar Scheuermann’s Kyphosis Compared With Thoracic Type\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eScheuermann\u0026rsquo;s kyphosis (SK) is classically defined by the presence of at least three contiguous vertebral bodies with \u0026ge;\u0026thinsp;5\u0026deg; of anterior wedging.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] SK has been categorized into typical and atypical forms [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The typical variant usually has an apex in the mid-thoracic region (T7\u0026ndash;T9) and satisfies Sorensen\u0026rsquo;s criteria, whereas the atypical variant more frequently presents with an apex at the thoracolumbar or lumbar levels, where classic radiographic findings such as disc space narrowing, endplate irregularities, and Schmorl\u0026rsquo;s nodes may be present, although Sorensen\u0026rsquo;s criteria are not always fulfilled [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe balance between the pelvis and spine is essential in determining a person's overall global spinal alignment.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Despite being viewed as a fixed anatomical measurement [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], pelvic incidence (PI) has been shown to have the potential for change in recent studies. [3,1,4໿]. However, these findings are mostly reported in adult deformity cohorts, and postoperative PI behavior in adolescent Scheuermann\u0026rsquo;s kyphosis remains unclear [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The literature has not clearly compared the effects of thoracic (TSK) versus thoracolumbar (TLSK) variants on PI, and the role of fusion extending into the lower lumbar segments is still uncertain [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This study therefore aims to address this gap by evaluating PI variability and its relationship with other spinopelvic parameters in SK subtypes.\u003c/p\u003e"},{"header":"MATERIAL AND METHOD","content":"\u003cp\u003e\u003cb\u003ePatient Selection and Clinical Investigation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollowing ethics committee approval, 102 patients with Scheuermann kyphosis who underwent single-stage posterior pedicle screw fusion between January 2015 and March 2021 were retrospectively reviewed. Inclusion criteria were: 1) patients diagnosed with SK under the age of 20 according to Sorensen criteria [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]; 2) visibility of the pelvis, femoral heads, and C2 vertebral body on standing whole spine AP and lateral radiographs in the preoperative and postoperative periods; 3) follow-up period of at least two years, ; Exclusion criteria were: patients with postural kyphosis, congenital spinal deformity, neuromuscular disease, patients with previous spine surgery or anterior spine surgery and patients who undergo revision spine surgery. According to the aforementioned criteria, 52 patients with Scheuermann kyphosis were included in the study.Thirty healthy participants of similar age were chosen from our database to form the control group.\u003c/p\u003e\u003cp\u003ePatients were divided into two groups according to the kyphotic apex vertebra level: Thoracic Scheuermann Kyphosis (TSK) and Thoracolumbar Scheuermann Kyphosis (TLK) Thoracic kyphosis (TSK) group, apex vertebra T9 or higher; The thoracolumbar kyphosis (TLSK) group was divided into those with apex vertebra T10 or below. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. All operated patients had a preoperative global kyphosis angle greater than 70°.The lowest instrumented vertebra was selected as the sagittal stable vertebra (SSA) or SSA-1, while the upper instrumented vertebra (UIV) was chosen as the proximal end vertebra (PEV) of the kyphotic deformity. Accordingly, 30 patients were included in the TSK group, 22 in the TLSK group, and 30 in the control group.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRadiological Evaluation\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePreoperative and final follow-up radiographs of the patients were taken on long cassettes in the posteroanterior and lateral planes while standing using standard procedures. Radiological measurements were evaluated using the SURGİMAP (Nemaric Inc., USA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.surgimap.com/\u003c/span\u003e\u003cspan address=\"https://www.surgimap.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) measurement system, and all measurements were meticulously performed by two independent spine surgeons.Nine spinopelvic parameters [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] on standing lateral view radiograms were evaluated at the preoperative and final follow-up assessments: 1) thoracic kyphosis (TK, T5–T12); 2) thoracolumbar kyphosis (TLK, T10–L2); 3) cervical lordosis (CL, C2–C7 Cobb angle); 4) lumbar lordosis (LL, L1–S1); 5) pelvic incidence (PI); 6) sacral slope (SS); 7) pelvic tilt (PT); 8) C7 sagittal vertical axis (SVA); 9 )PI-LL mismatch.\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003e)\u003c/b\u003e All data were given the mean standard error of three separate measurements, and categorical variables were reported as percentages. To limit interobserver variability, radiographs were assessed separately by two authors of the current study. An experienced spine surgeon also reviewed all medical records and radiographs in each instance.\u003c/p\u003e\n\n"},{"header":"STATISTICAL ANALYSIS","content":"\u003cp\u003eAll data were analyzed using the NCSS (Number Cruncher Statistical System) 2020 Statistical Software (NCSS LLC, Kaysville, Utah, USA)\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e Quantitative variables were presented as mean, standard deviation, median, minimum, and maximum values, while qualitative variables were expressed as frequencies and percentages. The Shapiro–Wilk test and Box plot graphics were used to assess the normality of distribution.For comparisons between two groups, the Student’s t-test was applied to normally distributed variables, whereas the Mann–Whitney U test was used for non-normally distributed variables. Relationships between continuous variables were evaluated with the Pearson correlation test. For paired measurements, the Paired Samples t-test was used in normally distributed data. Qualitative data were compared using the Chi-square test or the Fisher’s Freeman–Halton test where appropriate. A multivariate linear regression analysis was performed to determine the effects of spinopelvic parameters on pelvic incidence (PI). Results were interpreted at a 95% confidence interval, and a p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe mean follow-up duration of the patients included in the study was 4.6 ± 2.2 years, and the mean fusion segment length was 11.4 ± 0.9; no significant difference was detected between the groups (p \u0026lt; 0.05). Demographic data are shown in Table\u0026nbsp;1. No significant differences were found between groups (p = 0.41).When the apex vertebrae of the cases were examined, it was determined that in 30 patients the apex was at T9 or above, and in 22 patients it was at T10 or below (Table\u0026nbsp;1). Compared with the control group, the preoperative values of TK, TLK, GK, LL, and CL were significantly higher in both TLSK and TSK patients (p \u0026lt; 0.05), whereas PT and SVA values did not show a significant difference (p \u0026gt; 0.05). In TSK patients, no significant differences were found in PI and SS values compared to the control group preoperatively (p \u0026gt; 0.05); however, in TLSK patients, PI and SS values were found to be significantly lower (p \u0026lt; 0.05) (Table\u0026nbsp;2).Preoperatively, the PI and SS values of the TLSK group were significantly lower compared with the TSK group (p \u0026lt; 0.05), but no significant differences were detected postoperatively (p \u0026gt; 0.05). In addition, a significant postoperative increase was found in the PI and SS values in the TLSK group (p = 0.011). Among the TLSK and TSK groups, SVA, TK, and TLK values showed significant differences preoperatively; however, in the final follow-up, no significant differences were found between these parameters (p \u0026gt; 0.05). When sagittal radiological measurements were compared between the two groups, only the CL value showed a difference at the final follow-up. While preoperative CL values did not show a significant difference, at the final follow-up the CL value in the TLSK group was found to be significantly lower (p = 0.012) (Table\u0026nbsp;3).In the TLSK group, a statistically strong positive correlation was observed between ΔPI values and ΔPT values, and between ΔSS values and ΔCL values (r = 0.731, p = 0.001; r = 0.598, p = 0.003, respectively). A statistically moderate negative correlation was found between ΔLL values and ΔCL values (r = − 0.425; p = 0.045) (Table\u0026nbsp;4).In the univariate analysis of risk factors, preoperative PT, preoperative SS, preoperative LL, preoperative PI–LL, preoperative TLK, preoperative CL, and preoperative SVA measurements were found to be individually effective risk factors on preoperative PI measurement. According to the model summary showing the degrees of effect of the risk factors on preoperative PI measurement (R² = 0.945), the risk factors affected the preoperative PI measurement by 94.5% (Table\u0026nbsp;5).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"94%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1: Distributions of Descriptive Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43 (52,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39 (47,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16,85±1,65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMedian (Min–Max)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17 (13-19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up duration (years) (n=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4,60±2,21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMedian (Min–Max)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (2-8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eApex vertebra (n=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (3,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (26,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (3,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (13,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (21,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (7,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength of fused segments (n=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11,44±0,96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMedian (Min–Max)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (9-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLSK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22 (26,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSK\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30 (36,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30 (36,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv\u003e\n \u003cdiv align=\"left\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3: Comparison of Radiological Parameters by Groups\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLSK (n=22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSK (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37,77±10,42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44,87±8,89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,011*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal follow-up (n=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43,95±12,46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e45,83±10,92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,566\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,011*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,528\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange (Δ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6,18±10,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,97±8,29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,049*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11,95±10,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11,07±7,48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,716\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal follow-up (n=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13,82±7,89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12,60±8,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,591\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,394\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,163\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange (Δ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,86±10,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1,53±5,87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,882\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25,73±7,29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32,53±9,90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,009**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal follow-up (n=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30,18±11,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e33,3±8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,246\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,011*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,690\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange (Δ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4,45±7,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0,77±10,41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,163\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-63,09±14,31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-69,17±10,75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,086\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal follow-up (n=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-51,77±12,33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-50,8±12,37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,780\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,003**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,001**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange (Δ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11,32±16,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18,37±9,60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,054\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePI–LL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-24,73±17,41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-23,17±11,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,695\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal follow-up (n=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-8,05±11,54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-5,4±11,23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,411\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,001**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,001**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange (Δ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16,68±17,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17,77±10,99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,784\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44,77±11,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22,23±12,79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,001**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal follow-up (n=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12,36±10,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9,63±9,57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,325\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,001**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,001**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange (Δ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-32,41±16,37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-12,60±13,98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,001**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e62,77±11,52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e53,57±10,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,004**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal follow-up (n=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32,73±7,38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30,17±9,89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,312\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,001**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,001**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange (Δ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-30,05±13,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-23,40±11,82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,062\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-22,18±15,91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-26,20±18,86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,422\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal follow-up (n=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-11,55±15,81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-24,07±17,82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,012*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,028*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,550\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange (Δ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10,64±21,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2,13±19,30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,139\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-5,82±43,69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-28,23±25,91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,025*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal follow-up (n=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-18,23±16,61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-23,33±41,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,586\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,183\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,525\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange (Δ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMean ± SD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-12,41±42,31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4,90±41,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,148\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/em\u003e\u003cem\u003eStudent-t Test\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/em\u003e\u003cem\u003ePaired Samples t-Test\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e*p\u0026lt;0,05\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e**p\u0026lt;0,01\u0026nbsp;\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4: The Relationship Between Preoperative-Postoperative Radiological Parameters According to TLSK\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"934\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLSK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ PI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ PT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ SS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ LL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ PI–LL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ TLK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ TK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ CL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ SVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ PI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,001**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,054\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,357\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,045*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,896\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,928\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,816\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,428\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ PT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,001**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,162\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,499\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,007**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,077\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,112\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,081\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,527\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ SS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,054\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,162\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,013*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,422\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,055\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,056\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,003**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,036*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ LL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,357\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,499\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,013*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,001**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,565\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,433\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,049*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,045*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ PI–LL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,045*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,007**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,422\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,001**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,706\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,378\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,140\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,254\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ TLK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,433\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,896\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,077\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,055\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,565\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,706\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,023*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,120\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,044*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ TK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,928\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,112\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,056\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,433\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,378\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,023*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,280\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,088\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ CL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,816\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,081\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,003**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,049*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,140\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,120\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,280\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,003**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆ SVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0,372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0,609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,428\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,527\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,036*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,045*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,254\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,044*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,088\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0,003**\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cem\u003er:Pearson Correlation Test\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e**p\u0026lt;0,01\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e*p\u0026lt;0,05\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 5. Model Summary\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"359\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eR Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCorrected R square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,972 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0,941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eAs a result of the regression analysis, preoperative PT, preoperative SS, preoperative LL, and preoperative PI–LL measurements were found to have significant effects in the model. Among these, the greatest effect originated from preoperative LL, followed by preoperative PI–LL, preoperative PT, and preoperative SS (Table 6).\u003c/p\u003e\n\u003cdiv\u003eRegression Model:\u003c/div\u003e\n\u003cp\u003ePreoperative PI = 0.732 + 0.443 (Preop PT) + 0.331 (Preop SS) – 0.618 (Preop LL) + 0.554 (Preop PI–LL)\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003ePelvic incidence (PI) has traditionally been regarded as a fixed parameter, yet recent studies have challenged this view [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Jiang et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] emphasized that spinopelvic parameters in Scheuermann\u0026rsquo;s kyphosis vary according to deformity type. In line with their findings, our study demonstrated that TLSK patients had significantly lower PI and TLK values compared with TSK patients. They attributed these differences to rigid kyphosis and compensatory hyperlordosis, particularly in the lumbar and cervical regions. Jansen et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] also reported that the rate of LL improvement was greater in TSK than TLSK, which is consistent with our observation of less lumbar hyperlordosis in TLSK cases.\u003c/p\u003e\u003cp\u003eRizkallah et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] demonstrated that patients with low baseline PI (\u0026lt;\u0026thinsp;40\u0026deg;) exhibited greater postoperative changes, whereas those with higher PI values showed minimal variation. Consistent with these findings, our TLSK cohort\u0026mdash;characterized by a mean preoperative PI of 37.7\u0026deg;\u0026mdash;demonstrated a significant postoperative increase. Bederman et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] further suggested that in patients with low PI values, sagittal balance is maintained through adaptive changes in lumbar lordosis (LL) and pelvic tilt (PT). The postoperative increase in PI and sacral slope (SS) observed in our TLSK group supports this compensatory mechanism. While higher PI values are generally associated with greater compensatory capacity, excessively elevated PI has been linked to degenerative spondylolisthesis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This underscores the clinical relevance of understanding PI dynamics in deformity surgery. Similarly, Li et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported lower PI values (mean 35.1\u0026deg;) in patients with congenital or angular kyphosis secondary to tuberculosis, further corroborating our finding that TLSK patients present with inherently lower PI compared with both TSK patients and healthy controls.\u003c/p\u003e\u003cp\u003eWe observed that both TLSK and TSK patients presented with significantly elevated preoperative TK, TLK, GK, LL, and CL values relative to controls, implying that compensatory adaptations in Scheuermann\u0026rsquo;s kyphosis primarily occur in the lumbar and cervical regions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, PI and SS values in the TLSK group were significantly lower than those in both controls and the TSK group, consistent with the findings of Li et al., who reported kyphotic cases presenting with low PI values [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, the preoperative differences observed in SVA, TK, and TLK became comparable postoperatively, highlighting the effectiveness of surgery in restoring global sagittal balance [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. At the final follow-up, only CL was significantly lower in the TLSK group, suggesting that thoracolumbar correction may limit the adaptive capacity of the cervical spine; similarly, Liu et al. reported a tendency toward decreased cervical lordosis following thoracolumbar kyphosis surgery [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the TLSK group, strong associations were identified among spinopelvic parameters. Specifically, ∆PI correlated positively with ∆PT, ∆SS correlated positively with ∆CL, while ∆LL correlated negatively with ∆CL. These results indicate that changes in lumbar and pelvic alignment are closely linked to compensatory mechanisms at the cervical level. Our findings are consistent with those of Ye et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], who reported that increased lumbar lordosis may be accompanied by a compensatory reduction in cervical lordosis.\u003c/p\u003e\u003cp\u003eIn our study, univariate analysis demonstrated that preoperative PT, SS, LL, PI-LL, TLK, CL, and SVA significantly influenced PI, and the model explained PI with 94.5% accuracy. The strongest effect originated from LL, suggesting that PI should not be regarded as a fixed congenital value but rather as a dynamic parameter shaped within the spinopelvic chain. Similarly, Bederman et al. reported strong associations of LL and PT with PI in patients with Scheuermann\u0026rsquo;s disease [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Nasto et al. also emphasized PI and LL as critical predictive parameters in preoperative spinopelvic alignment [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study has certain limitations. The retrospective design inherently carries the risk of selection bias, even though strict inclusion and exclusion criteria were applied. The relatively small sample size may have reduced the statistical power, especially for subgroup comparisons. Radiographic measurements were based on standing lateral radiographs, which do not reflect dynamic or functional changes in spinopelvic parameters during motion or in different positions. Although measurements were performed by two independent observers, the possibility of interobserver variability cannot be entirely excluded. Furthermore, the follow-up period, while sufficient to evaluate short- to mid-term outcomes, may not fully demonstrate the long-term adaptations of pelvic incidence and sagittal balance. These limitations should be considered when interpreting our results, and future prospective, multicenter studies with larger patient populations and longer follow-up are warranted.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study demonstrates that PI significantly increased after surgery in patients with thoracolumbar Scheuermann kyphosis, whereas no such change was observed in thoracic cases. The extension of fusion into the lower lumbar spine may contribute to this alteration, particularly in TLSK patients with lower baseline PI values. For surgical planning, PI should be assessed together with LL, PT, SS, and the PI\u0026ndash;LL mismatch rather than in isolation. Particular attention is required in TLSK patients to maintain sagittal balance, as neglecting PI dynamics may predispose to iatrogenic spondylolisthesis. These findings highlight the importance of comprehensive spinopelvic evaluation in Scheuermann kyphosis surgery.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e●MFS: Data curation, formal analysis, visualization, investigation, resources, project administration, and writing \u0026ndash; original draft. Collected, analyzed, and interpreted the data, prepared figures and tables, managed project administration, and drafted the initial manuscript.●OO: Conceptualization, methodology, and writing \u0026ndash; original draft. Developed the research concept and study design, and contributed to manuscript drafting.●FS: Collected, analyzed, and interpreted the data, prepared figures and tables, managed project administration, and drafted the initial manuscript.●AK: Provided oversight and leadership, validated findings, and critically reviewed and revised the manuscript.●MEİ: Supervision, validation, and writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLonner BS, Toombs CS, Mechlin M, Ciavarra G, Shah SA, Samdani AF, et al. MRI screening in operative Scheuermann kyphosis: is it necessary? Spine Deform. 2017;5(2):124\u0026ndash;33. doi:10.1016/j.jspd.2016.10.008\u003c/li\u003e\n\u003cli\u003eSardar ZM, Ames RJ, Lenke L. Scheuermann\u0026rsquo;s kyphosis: diagnosis, management, and selecting fusion levels. J Am Acad Orthop Surg. 2019;27(10):e462\u0026ndash;72. doi:10.5435/JAAOS-D-17-00748\u003c/li\u003e\n\u003cli\u003ePlace HM, Hayes AM, Huebner SB, Hayden AM, Israel H, Brechbuhler JL. Pelvic incidence: a fixed value or can you change it? Spine J. 2017;17(10):1565\u0026ndash;9. doi:10.1016/j.spinee.2017.06.037\u003c/li\u003e\n\u003cli\u003eMaekawa A, Endo K, Suzuki H, Sawaji Y, Nishimura H, Matsuoka Y, et al. Impact of pelvic incidence on change in lumbo-pelvic sagittal alignment between sitting and standing positions. Eur Spine J. 2019;28(9):1914\u0026ndash;9. doi:10.1007/s00586-019-05891-9\u003c/li\u003e\n\u003cli\u003eOhya J, Kawamura N, Takasawa E, Onishi Y, Ohtomo N, Miyahara J, et al. Pelvic incidence change on the operating table. Eur Spine J. 2021;30(9):2473\u0026ndash;9. doi:10.1007/s00586-021-06753-z\u003c/li\u003e\n\u003cli\u003eSchwab F, Lafage V, Patel A, Farcy JP. Sagittal plane considerations and the pelvis in the adult patient. Spine (Phila Pa 1976). 2009;34(17):1828\u0026ndash;33. doi:10.1097/BRS.0b013e3181a13c08\u003c/li\u003e\n\u003cli\u003eLin G, Wang S, Yang Y, Su Z, Du Y, Xu X, et al. The effect of pedicle subtraction osteotomy for the correction of severe Scheuermann thoracolumbar kyphosis on sagittal spinopelvic alignment. BMC Musculoskelet Disord. 2021;22(1):165. doi:10.1186/s12891-020-03942-7\u003c/li\u003e\n\u003cli\u003eMenezes CM, Lacerda GC, Lamarca S. Sagittal alignment concepts and spinopelvic parameters. Rev Bras Ortop. 2023;58(1):1\u0026ndash;8. doi:10.1055/s-0042-1742602\u003c/li\u003e\n\u003cli\u003eRoussouly P, Gollogly S, Noseda O, Berthonnaud E, Dimnet J. The vertical projection of the sum of the ground reactive forces of a standing patient is not the same as the C7 plumb line: a radiographic study of the sagittal alignment of 153 asymptomatic volunteers. Spine (Phila Pa 1976). 2006;31(11):E320\u0026ndash;5. doi:10.1097/01.brs.0000218263.58642.ff\u003c/li\u003e\n\u003cli\u003eRizkallah M, Shen J, Phan P, Al-Shakfa F, Kamel Y, Liu J, et al. Can pelvic incidence change after lumbo-pelvic fixation for adult spine deformity, and would the change be affected by the type of pelvic fixation? Spine (Phila Pa 1976). 2024;49(1):E1\u0026ndash;7. doi:10.1097/BRS.0000000000004651\u003c/li\u003e\n\u003cli\u003eSchroeder N, Noschenko A, Burger E, Patel V, Cain C, Ou-Yang D, et al. Pelvic incidence changes between flexion and extension. Spine Deform. 2018;6(6):753\u0026ndash;61. doi:10.1016/j.jspd.2018.03.008\u003c/li\u003e\n\u003cli\u003eJiang L, Qiu Y, Xu L, et al. Sagittal spinopelvic alignment in adolescents associated with Scheuermann\u0026rsquo;s kyphosis: a comparison with normal population. Eur Spine J. 2014;23(7):1420\u0026ndash;6. doi:10.1007/s00586-014-3266-2\u003c/li\u003e\n\u003cli\u003eLi W, Sun Z, Guo Z, Qi Q, Kim SD, Zeng Y, et al. Analysis of spinopelvic sagittal alignment in patients with thoracic and thoracolumbar angular kyphosis. Spine (Phila Pa 1976). 2013;38(13):E813\u0026ndash;8. doi:10.1097/BRS.0b013e3182913219\u003c/li\u003e\n\u003cli\u003eBlumenthal SL, Roach J, Herring JA. Lumbar Scheuermann\u0026rsquo;s: a clinical series and classification. Spine (Phila Pa 1976). 1987;12(9):929\u0026ndash;32. doi:10.1097/00007632-198712000-00002\u003c/li\u003e\n\u003cli\u003eLe Huec JC, Thompson W, Mohsinaly Y, Barrey C, Faundez A. Sagittal balance of the spine. Eur Spine J. 2019;28(9):1889\u0026ndash;905. doi:10.1007/s00586-019-06083-1\u003c/li\u003e\n\u003cli\u003eJansen RC, van Rhijn LW, van Ooij A. Predictable correction of the unfused lumbar lordosis after thoracic correction and fusion in Scheuermann kyphosis. Spine (Phila Pa 1976). 2006;31(11):1227\u0026ndash;31. doi:10.1097/01.brs.0000217682.53629.ad\u003c/li\u003e\n\u003cli\u003eBederman SS, Farhan S, Hu X, Lieberman IH, Belanger TA, Musa A, et al. Sagittal spinal and pelvic parameters in patients with Scheuermann\u0026rsquo;s disease: a preliminary study. Int J Spine Surg. 2019;13(6):536\u0026ndash;43. doi:10.14444/6073\u003c/li\u003e\n\u003cli\u003eOh SK, Chung SS, Lee CS. Correlation of pelvic parameters with isthmic spondylolisthesis. Asian Spine J. 2009;3(1):21\u0026ndash;6. doi:10.4184/asj.2009.3.1.21\u003c/li\u003e\n\u003cli\u003eBarrey C, Jund J, Perrin G, Roussouly P. Spinopelvic alignment of patients with degenerative spondylolisthesis. Neurosurgery. 2007;61(5):981\u0026ndash;6; discussion 986. doi:10.1227/01.neu.0000303194.02921.30\u003c/li\u003e\n\u003cli\u003eYe J, Rider SM, Lafage R, et al. Spinopelvic sagittal compensation in adult cervical deformity. \u003cem\u003eJ Neurosurg Spine\u003c/em\u003e. 2023;39(1):1-10. Published 2023 Mar 24. doi:10.3171/2023.2.SPINE221295\u003c/li\u003e\n\u003cli\u003eLiu, C., Zhu, W., Li, Y. \u003cem\u003eet al.\u003c/em\u003e How does cervical sagittal profile change after the spontaneous compensation of global sagittal imbalance following one- or two-level lumbar fusion. \u003cem\u003eBMC Musculoskelet Disord\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 387 (2024). https://doi.org/10.1186/s12891-024-07518-7\u003c/li\u003e\n\u003cli\u003eNasto LA, Jain A, Shalabi A, et al. \u003cem\u003eCorrelation between preoperative spinopelvic alignment and risk of proximal junctional kyphosis after posterior-only surgical correction of Scheuermann kyphosis.\u003c/em\u003e Spine J. 2016;16(10):1239-1248. DOI: 10.1016/j.spinee.2016.02.042\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7889500/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7889500/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003ePelvic incidence(PI) has traditionally been regarded as a fixed anatomical parameter. However, emerging evidence suggests that PI may change following spinal deformity correction. Comparative data on postoperative spinopelvic adaptations in thoracic(TSK) versus thoracolumbar(TLSK) Scheuermann kyphosis, and their deviation from healthy populations, remain limited. This study aimed to compare preoperative spinopelvic and sagittal alignment parameters between TSK and TLSK, evaluate their differences relative to healthy controls, and investigate postoperative changes and interrelationships among these parameters.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eFifty-two patients with Scheuermann kyphosis(30 TSK, 22 TLSK; mean follow-up 4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 years) were retrospectively analyzed and compared with 30 age-matched healthy controls. Radiographic parameters included thoracic kyphosis(TK), thoracolumbar kyphosis(TLK), lumbar lordosis(LL), cervical lordosis(CL), pelvic incidence(PI), sacral slope(SS), pelvic tilt(PT), sagittal vertical axis(SVA), and PI\u0026ndash;LL mismatch. Measurements were obtained preoperatively, postoperatively, and at final follow-up. Group comparisons, correlation analyses, and multivariate regression were performed to identify predictors of PI.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eTLSK patients demonstrated significantly lower preoperative PI and SS compared with TSK and controls(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Both SK groups exhibited greater TK, TLK, LL, and CL compared with controls, while PT and SVA were similar. Following surgery, TLSK patients showed a significant increase in PI(p\u0026thinsp;=\u0026thinsp;0.011) and SS, eliminating preoperative differences between groups. Preoperative disparities in SVA, TK, and TLK resolved by final follow-up, whereas CL remained significantly lower in TLSK(p\u0026thinsp;=\u0026thinsp;0.012). Regression analysis identified LL, PI\u0026ndash;LL mismatch, PT, and SS as significant predictors of PI(R\u0026sup2;=0.945), with LL exerting the strongest influence.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eSurgical correction achieves restoration of global sagittal balance in both TSK and TLSK. PI, long considered immutable, may increase postoperatively in TLSK, highlighting its dynamic behavior. Comprehensive preoperative assessment of sagittal alignment\u0026mdash;particularly PI and LL\u0026mdash;is crucial for optimal surgical planning in Scheuermann kyphosis.\u003c/p\u003e","manuscriptTitle":"Pelvic Incidence is Not Fixed: Postoperative Changes in Thoracolumbar Scheuermann’s Kyphosis Compared With Thoracic Type","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 08:09:40","doi":"10.21203/rs.3.rs-7889500/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8057d4c9-8519-4bc6-820d-81d220bd1ee8","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-02T08:55:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 08:09:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7889500","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7889500","identity":"rs-7889500","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}