How Deep Is Their Understanding? The Illusion of Explanatory Depth in Pediatric Dentistry Training

How Deep Is Their Understanding? 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The Illusion of Explanatory Depth in Pediatric Dentistry Training Dusan Surdilovic, Vivek Padmanabhan, Md Sofiqul Islam, Muhammed Mustahsen Rahman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8262006/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: This study examined the Illusion of Explanatory Depth (IOED) among final-year dental students in pediatric dentistry by comparing perceived explanatory understanding with objectively evaluated explanatory performance and diagnostic accuracy. The goal was to determine whether IOED represents a metacognitive vulnerability that may contribute to diagnostic errors in pediatric dental decision-making. Methods: A cross-sectional explanatory design was used with 142 final-year Bachelor of Dental Surgery students across two academic years. Students rated their perceived explanatory understanding (PEUS) for three pediatric topics (caries risk assessment, pulp diagnosis, behaviour guidance). They then completed structured written explanations scored using a validated rubric to generate Observed Explanatory Performance Scores (OEPS). The Explanatory Calibration Index (ECI = PEUS - OEPS×2) quantified the degree of over- or underestimation. Diagnostic accuracy was assessed through six pediatric key-feature vignettes (DAS). Associations among PEUS, OEPS, ECI, and DAS were examined using paired comparisons and Pearson correlations. Results: Students consistently overestimated their explanatory understanding across all topics, with the largest IOED effect observed in behaviour guidance. Mean global ECI indicated substantial overestimation. OEPS showed a moderate positive correlation with diagnostic accuracy (r = 0.47, p < .001), whereas PEUS did not (r = 0.12, p = .14). Higher ECI values were negatively associated with diagnostic accuracy (r = –0.31, p < .001), suggesting that greater overestimation was linked to poorer clinical reasoning performance. Conclusions: Dental students systematically overestimate the depth of their understanding in pediatric dentistry, and IOED appears to function as a meaningful metacognitive risk factor. Explanatory performance - not perceived understanding, was the reliable predictor of diagnostic accuracy. Incorporating structured explanation tasks, metacognitive calibration activities, and targeted feedback into pediatric dental education may help align students’ perceived and actual understanding, improving diagnostic performance and supporting safer clinical practice. Illusion of Explanatory Depth Metacognition Explanatory Accuracy Diagnostic Reasoning Calibration Pediatric Dentistry Education Clinical Decision-Making Health Professions Education Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction High-quality dental education increasingly relies on students’ ability not only to acquire knowledge but also to accurately evaluate the depth of their own understanding-an essential component of metacognition. In clinical fields such as pediatric dentistry, where diagnostic reasoning, behavior management, and and treatment planning require layered causal understanding, a learner’s ability to judge what they truly understand directly affects clinical accuracy, patient safety, and the development of professional judgment. However, substantial evidence suggests that learners commonly hold inflated beliefs about the depth of their understanding, particularly in domains involving complex mechanisms. This bias is known as the Illusion of Explanatory Depth (IOED), first described by Rozenblit and Keil, who demonstrated that people systematically overestimate how well they can explain causal systems once they attempt to articulate them in detail(Rozenblit & Keil, 2002 ). Metacognitive accuracy plays a crucial role in health professions education. Students who can accurately judge their understanding tend to make better clinical decisions, seek information more efficiently, and avoid premature diagnostic closure(Eva & Regehr, 2007 ). In medicine, studies show that diagnostic precision improves when learners recognize limits in their understanding(Friedman et al., 2005 ). In dentistry, similar evidence indicates that miscalibrated judgments-where perceived competence exceeds actual performance-can negatively affect treatment planning and patient outcomes(Chaffin et al., 2002 ). These observations align with broader cognitive research demonstrating that individuals often rely on superficial cues such as familiarity or fluency when estimating their own understanding, rather than true conceptual mastery(Alter & Oppenheimer, 2009 ). Most dental-education research examining metacognition has focused on self-assessment, calibration of confidence, or general overconfidence bias. A substantial body of work has documented that dental students frequently misjudge their competence during preclinical and clinical tasks(Manogue & Brown, 2010 ). In addition, evidence from assessment research shows that global self-evaluations rarely correlate strongly with objective performance measures(Schuwirth & van der Vleuten, 2004 ). While these constructs are informative, they differ from IOED. Overconfidence reflects a mismatch between confidence and correctness; self-assessment captures broad perceptions of competence. IOED, however, specifically explores the discrepancy between perceived explanatory depth and actual ability to generate accurate, coherent explanations. Pediatric dentistry offers a rich context for exploring IOED. Topics such as caries risk assessment, pulp diagnosis in children, space management, or behavior guidance require students to integrate biological, developmental, and behavioral principles. Yet, research shows that students often display confidence levels that surpass their diagnostic accuracy in pediatric clinical scenarios(Divaris et al., 2008 ). Additional studies identify systematic errors in clinical judgment among dental students managing pediatric cases(Moura et al., 2016 ). Such findings suggest that IOED may be an underlying cognitive mechanism contributing to these discrepancies. Further, contemporary dental curricula increasingly emphasize active learning and case-based strategies intended to foster deeper understanding. Yet empirical evidence shows that even after interactive learning experiences, students may feel they understand a topic more deeply than they can actually explain it(Michael, 2006 ). Psychological research indicates that generating explanations is one of the most sensitive methods for revealing gaps in understanding-far more effective than recognition-based tasks(Lombrozo, 2006 ). This makes IOED particularly relevant for understanding how students internalize, structure, and apply knowledge in pediatric dentistry. Metacognition also influences clinical competency performance. In medical education, students demonstrating stronger metacognitive regulation tend to perform better on OSCEs and exhibit more accurate diagnostic reasoning(Artino et al., 2012 ). Conversely, diagnostic error research identifies overestimation of understanding as a contributor to reasoning shortcuts and premature closure(Berner & Graber, 2008 ). These insights are highly relevant to pediatric dentistry, where decisions must account for child behavior, developmental considerations, and the modification of adult-based diagnostic frameworks. Despite the importance of explanatory accuracy in clinical decision-making, IOED remains unexplored in dental education. No published study has systematically examined the discrepancy between perceived and actual explanatory depth among dental students, particularly within a pediatric context. Addressing this gap is essential because pediatric dentistry requires students to apply complex reasoning to real-world clinical problems involving children, where misunderstanding can directly influence treatment quality. Therefore, the present study aims to evaluate the Illusion of Explanatory Depth among final-year dental students by comparing their self-perceived understanding with their demonstrated explanatory accuracy across core pediatric dentistry concepts. By quantifying the magnitude of IOED and identifying its association with explanatory performance, this study seeks to reveal metacognitive vulnerabilities and inform targeted improvements in pediatric dentistry training. Aim of the study The aim of this study was to investigate the Illusion of Explanatory Depth (IOED) among final-year dental students in pediatric dentistry by (i) comparing their self-rated explanatory understanding of core pediatric topics with their objectively rated explanatory performance, (ii) quantifying the calibration gap between perceived and actual explanatory depth, (iii) evaluating topic-specific differences across caries risk assessment, pulp diagnosis, and behaviour guidance, and (iv) examining the association between explanatory depth and diagnostic accuracy in pediatric clinical scenarios. Methodology Study design and context This study employed a cross-sectional explanatory design integrating self-assessed understanding, structured explanatory tasks, calibration analysis, and diagnostic accuracy testing. Multi-component metacognitive designs of this type are recommended when investigating discrepancies between perceived and actual understanding in health professions education, because they allow simultaneous examination of subjective judgments, explanatory performance, and applied clinical reasoning(Veenman, 2011 ). The study was conducted in the Department of Pediatric Dentistry at a dental school in the Middle East during two consecutive academic years, providing a stable curricular context and comparable instructional exposure for all participating students. Participants The target population comprised final-year (5th year) Bachelor of Dental Surgery (BDS) students enrolled in the pediatric dentistry clinical course. Across the two academic years, 148 students were eligible to participate. Inclusion criteria were: active enrollment in the pediatric dentistry course, completion of all core theoretical modules related to pediatric caries, pulp therapy in primary teeth, and behaviour guidance, no ongoing remedial status or prolonged absence from clinical duties. Participation was voluntary, and all students provided written informed consent. Sample sizes exceeding approximately 100 participants are considered adequate for stable estimation of metacognitive indices and reliability of performance-based measures in educational research(Kline, 2015 ). Selection of pediatric topics Three core pediatric domains were selected as IOED targets: Caries risk assessment in children Pulp diagnosis and management in primary teeth Behaviour guidance for anxious pediatric patients These domains were chosen because they require integration of biological, developmental, and behavioural concepts, and previous work has shown that dental students frequently display errors or inconsistencies in clinical judgment specifically in pediatric scenarios(Moura et al., 2016 ). The topics align with the existing pediatric dentistry curriculum, ensuring content familiarity for all final-year students. Measures 1. Perceived Explanatory Understanding Score (PEUS) PEUS captured students’ self-rated explanatory understanding prior to any explanatory or diagnostic task. For each of the three topics, students were asked: How well do you believe you can explain this concept to a peer? Each response was recorded on a 10-point numerical rating scale (1 = “not at all well”, 10 = “extremely well”). Single-item metacognitive judgments of this type are widely used as global indicators of perceived understanding and have been shown to be informative for calibration research(Dunlosky & Metcalfe, 2009 ). PEUS values were obtained for each topic separately (range 1–10). A global PEUS was computed as the arithmetic mean of the three topic-specific scores. 2. Observed Explanatory Performance Score (OEPS) OEPS represented students’ objectively rated explanatory ability. Following the canonical Illusion of Explanatory Depth (IOED) paradigm[1,18], students were asked to produce short written explanations for each of the three pediatric topics. Explanatory prompts Students responded in writing (4–6 sentences) to the following prompts: Explain the process of caries risk assessment in a 6-year-old child. Explain the diagnostic approach and treatment decision-making for pulp involvement in a primary molar. Explain the steps of behaviour guidance for an anxious child attending the dental clinic for the first time. These prompts were designed to elicit causal, conceptual, and procedural knowledge, consistent with prior research demonstrating that explanation tasks are highly sensitive to gaps in understanding(Chi et al., 1994 ). Scoring rubric and rating procedure Two pediatric dentistry faculty members independently rated each explanation using a 5-point analytic rubric, adapted from explanatory assessment frameworks in cognitive and educational research[19,20]. The rubric evaluated: Causal accuracy (correctness of key mechanisms and relationships), Conceptual completeness (coverage of essential elements), Logical structure (clarity and coherence of reasoning), Clinical relevance (appropriateness for pediatric practice). Each dimension was scored from 0 to 5, and an overall OEPS score (0–5) per topic was assigned based on holistic integration of these criteria, following established practices in performance-based explanation scoring(Lombrozo, 2006 ). Inter-rater reliability for OEPS was evaluated using a two-way random intraclass correlation coefficient (ICC), which is recommended for reliability studies involving multiple raters and continuous scores(Shrout & Fleiss, 1979 ). Discrepant ratings were resolved by discussion, but only scores from the independent ratings were used for ICC computation. A global OEPS was calculated as the mean of the three topic-specific scores. 3. Explanatory Calibration Index (ECI) The Explanatory Calibration Index (ECI) quantified the gap between perceived and actual explanatory depth, providing a direct operationalization of IOED. Calibration metrics similar to ECI have a long tradition in judgment and decision-making research and are widely used to assess metacognitive accuracy(Lichtenstein et al., 1982 ). Because PEUS was recorded on a 1–10 scale and OEPS on a 0–5 scale, OEPS values were first linearly transformed to a 0–10 metric by multiplication with two. For each topic, ECI was computed as: ECI = PEUS – (OEPS × 2) Interpretation: ECI > + 2 → overestimation (strong IOED pattern), ECI between − 1 and + 1 → good calibration, ECI < − 2 → underestimation (impostor-like pattern). ECI was calculated per topic and as a global index (mean of three ECI values). This allowed analysis of both topic-specific and overall metacognitive calibration. 4. Diagnostic Accuracy Score (DAS) DAS measured applied pediatric diagnostic accuracy, independent of self-perception or explanatory performance. Diagnostic accuracy tests are recognized as valid indicators of clinical reasoning quality in health professions education(Eva, 2005 ). Students completed six pediatric case-based micro-vignettes, constructed in alignment with key-feature format principles(Page & Bordage, 1995 ): 2 vignettes focused on caries risk assessment, 2 on pulp diagnosis and management in primary teeth, 2 on behaviour guidance in common pediatric scenarios. Each vignette was followed by a single best-answer question targeting the critical diagnostic or management decision. Responses were scored 0 (incorrect) or 1 (correct), yielding a total DAS range from 0 to 6. Higher scores indicated better diagnostic accuracy. Data collection procedure Data collection followed a fixed sequence, closely aligned with established IOED protocols[1,18]: PEUS ratings: students first rated their perceived explanatory understanding for each of the three topics. Explanatory tasks: students then wrote 4–6 sentence explanations for each topic, without access to notes or electronic resources. OEPS scoring: explanations were subsequently rated independently by two faculty members using the analytic rubric. Diagnostic vignettes: students completed the six pediatric micro-vignettes (DAS). All activities were conducted in a supervised classroom environment, during a single scheduled session, to standardize conditions and prevent consultation of external resources. The total time required to complete all components was approximately 35–40 minutes, which is comparable to durations reported in explanation-based metacognitive studies and educational assessment sessions(Lodge & Kennedy, 2013 ). Ethical considerations The study protocol was reviewed and approved by the institutional research ethics committee. Participation was voluntary, and students were informed that they could withdraw at any time without academic consequences. Data were anonymized prior to analysis; individual scores were not shared with course instructors in a way that could influence summative assessment. These procedures are consistent with best-practice recommendations for ethics in educational research involving students(Kass et al., 2007 ). Results Overview of analyses All analyses were conducted at the student level. Descriptive statistics were calculated for all primary variables: PEUS, OEPS, ECI, and DAS. Paired-samples tests were used to compare perceived explanatory understanding (PEUS) with scaled explanatory performance (OEPS×2) for each topic. Repeated-measures analyses examined topic-specific differences in IOED indicators. Calibration categories were derived from the ECI thresholds, and their distribution was summarized descriptively. Associations among explanatory measures and diagnostic accuracy were explored using Pearson’s correlation coefficients. A significance level of p < 0.05 was adopted for all tests. Participant characteristics Of the 148 eligible final-year students, 142 (95.9%) completed all components of the study and were included in the analysis. The sample had a mean age of 23.1 ± 1.2 years, and 62.0% of participants were female. The mean cumulative GPA was 3.21 ± 0.39 (on a 4-point scale). Baseline characteristics are summarized in Table 1 . Table 1 Participant characteristics (N = 142) Variable Value Age, years, mean ± SD 23.1 ± 1.2 Female, n (%) 88 (62.0) Male, n (%) 54 (38.0) Cumulative GPA (0–4), mean ± SD 3.21 ± 0.39 Academic year 1, n (%) 71 (50.0) Academic year 2, n (%) 71 (50.0) Inter-rater reliability for the OEPS ratings was high; the two-way random ICC for the overall explanatory score across all topics was 0.86 (95% CI 0.81–0.90), indicating excellent agreement between raters. Descriptive statistics for PEUS, OEPS, ECI and DAS Across the three pediatric topics, students reported relatively high levels of perceived explanatory understanding. Mean PEUS scores ranged from 7.2 to 8.1 on a 10-point scale. In contrast, observed explanatory performance (OEPS) mean scores ranged from 2.8 to 3.2 on the 0–5 scale. After rescaling OEPS to a 0–10 range, the mean global Explanatory Calibration Index (ECI) was + 1.7 ± 1.5, indicating a general tendency toward overestimation. The mean Diagnostic Accuracy Score (DAS) was 4.0 ± 1.1 out of 6, with scores spanning the full range from 0 to 6. Descriptive statistics for all variables by topic and globally are presented in Table 2 . Table 2 Descriptive statistics for PEUS, OEPS, ECI and DAS Measure Topic Scale Mean ± SD PEUS Caries risk assessment 1–10 7.8 ± 1.2 PEUS Pulp diagnosis 1–10 7.2 ± 1.4 PEUS Behaviour guidance 1–10 8.1 ± 1.1 PEUS (global) Mean of 3 topics 1–10 7.7 ± 1.0 OEPS Caries risk assessment 0–5 3.2 ± 0.8 OEPS Pulp diagnosis 0–5 2.8 ± 0.9 OEPS Behaviour guidance 0–5 3.0 ± 0.9 OEPS (global) Mean of 3 topics 0–5 3.0 ± 0.7 ECI Caries risk assessment approx − 5 to + 5 1.4 ± 1.7 ECI Pulp diagnosis approx − 5 to + 5 1.6 ± 1.8 ECI Behaviour guidance approx − 5 to + 5 2.1 ± 1.9 ECI (global) Mean of 3 topics approx − 5 to + 5 1.7 ± 1.5 DAS All vignettes (6 items) 0–6 4.0 ± 1.1 Topic-specific IOED patterns Paired comparisons between PEUS and scaled OEPS demonstrated a consistent Illusion of Explanatory Depth across all three topics. For caries risk assessment, the mean PEUS was 7.8 ± 1.2, whereas the scaled OEPS (OEPS×2) was 6.4 ± 1.5, yielding a significant mean difference of 1.4 points (p < 0.001). For pulp diagnosis and management, PEUS was 7.2 ± 1.4 and scaled OEPS 5.6 ± 1.6, a difference of 1.6 points (p < 0.001). The largest discrepancy was observed for behaviour guidance, where PEUS reached 8.1 ± 1.1, while scaled OEPS was 6.0 ± 1.7, corresponding to a mean difference of 2.1 points (p < 0.001). Global comparisons across topics using repeated-measures analysis indicated significant variation in calibration, with the highest ECI values observed in behaviour guidance. Post-hoc comparisons confirmed that behaviour guidance ECI was significantly higher than ECI for caries risk assessment and pulp diagnosis (both p < 0.01). These patterns are illustrated in Fig. 1 , which compares mean PEUS and scaled OEPS for each topic. Error bars represent standard deviations. Across all topics, PEUS scores exceeded scaled OEPS scores, indicating a consistent Illusion of Explanatory Depth among final-year dental students. The discrepancy was largest for behaviour guidance. Distribution of calibration categories: Based on the ECI thresholds, students were categorized as: Well calibrated (ECI between − 1 and + 1) Overestimators (ECI > + 2) Underestimators (ECI < − 2) For the global ECI, 58.5% of students fell into the overestimation category, 32.4% were well calibrated, and 9.2% underestimated their explanatory depth. Topic-specific distributions showed that overestimation was most prevalent in behaviour guidance, where 68.3% of students met the overestimation criterion, compared with 51.4% for caries risk assessment and 55.6% for pulp diagnosis. The distribution of global ECI values is presented as a density/histogram in Fig. 2 , and topic-specific calibration categories are summarized in Fig. 3 . Positive ECI values reflect overestimation of explanatory depth (IOED), while negative values reflect underestimation. Most students demonstrated positive ECI values, indicating a general tendency toward overestimation. Bars represent percentages of students showing underestimation, accurate calibration, or overestimation (ECI-based categories). Overestimation was most prevalent in behaviour guidance, followed by pulp diagnosis and caries risk assessment.) across caries risk assessment, pulp diagnosis, and behaviour guidance. These findings indicate that while a minority of students demonstrated accurate self-calibration, the majority displayed a systematic Illusion of Explanatory Depth, particularly in behaviour guidance scenarios. Associations between explanatory measures and diagnostic accuracy Correlation analyses revealed differential relationships among perceived understanding, explanatory performance, calibration, and diagnostic accuracy. There was a small but significant positive correlation between PEUS and OEPS (r = 0.22, p = 0.008), suggesting that students who perceived themselves as better explainers tended, to some extent, to explain more accurately. PEUS and DAS were not significantly correlated (r = 0.12, p = 0.14), indicating that higher perceived explanatory understanding did not reliably predict better diagnostic accuracy. In contrast, OEPS showed a moderate positive correlation with DAS (r = 0.47, p < 0.001), suggesting that students who provided higher-quality explanations also performed better on pediatric diagnostic vignettes. The ECI was negatively correlated with DAS (r = − 0.31, p < 0.001), indicating that greater overestimation of explanatory depth was associated with lower diagnostic accuracy. These relationships are summarized in Table 3 and visualized in Fig. 4 , which displays scatter plots of OEPS versus DAS and ECI versus DAS with regression lines. Table 3 Correlations among PEUS, OEPS, ECI and DAS (N = 142) Variables r p value PEUS (global) – OEPS 0.22 0.008 PEUS (global) – DAS 0.12 0.14 OEPS (global) – DAS 0.47 < 0.001 ECI (global) – DAS –0.31 < 0.001 (Pearson’s correlation coefficients.) Together, the two panels indicate that actual explanatory ability (OEPS) - not perceived understanding - is meaningfully related to clinical diagnostic performance, and that stronger Illusion of Explanatory Depth (higher ECI) may impair diagnostic reasoning in pediatric dentistry. Collectively, these results suggest that actual explanatory depth, rather than perceived understanding, is meaningfully associated with clinical diagnostic performance, and that stronger IOED (higher ECI) may represent a metacognitive risk factor for reduced diagnostic accuracy in pediatric dentistry. Discussion This study examined the Illusion of Explanatory Depth (IOED) among final-year dental students by comparing self-rated explanatory understanding with objectively evaluated explanatory performance and diagnostic accuracy in pediatric dentistry. Across all three core pediatric topics-caries risk assessment, pulp diagnosis, and behaviour guidance-students consistently overestimated the depth of their understanding, with the largest gap observed in behaviour guidance. This pattern mirrors the central premise of IOED theory: individuals believe they understand complex systems more deeply than they actually do once they attempt to explain them in detail(Rozenblit & Keil, 2002 ). The finding that perceived understanding (PEUS) was high across all topics yet showed only a weak association with diagnostic performance suggests that students’ subjective judgments are poor indicators of true conceptual mastery. IOED appears to function as a cognitive blind spot in pediatric dental training, particularly in domains requiring integration of behavioural and procedural reasoning. This aligns with evidence from cognitive psychology showing that learners often rely on feelings of familiarity or fluency rather than actual explanatory depth when making metacognitive judgments(Alter & Oppenheimer, 2009 ). In contrast, observed explanatory performance (OEPS) demonstrated a moderate positive association with diagnostic accuracy. This reinforces the central role of explanation quality as a cognitive marker of understanding. Explanation-based assessments have been shown to reveal conceptual gaps that recognition-based tasks fail to detect(Chi et al., 1994 ). In the current study, students who articulated clearer, more coherent explanations were also the ones who performed better on pediatric diagnostic scenarios. The Explanatory Calibration Index (ECI) provided strong evidence of metacognitive misalignment. More than half of the students demonstrated overestimation, and higher ECI values were associated with lower diagnostic accuracy. This inverse relationship suggests that IOED is not a benign cognitive quirk but may represent a metacognitive risk factor that undermines clinical reasoning. Similar patterns have been reported in medical diagnostic error literature, where overestimation contributes to premature closure and misjudgment(Berner & Graber, 2008 ). The current findings extend this phenomenon to pediatric dental education, highlighting the potential consequences of poorly calibrated explanatory understanding. Topic-specific differences offer further insight. Behaviour guidance consistently showed the largest IOED magnitude. This may reflect the inherently complex and less linear nature of behavioural reasoning in pediatric clinical practice, which requires situational judgment, emotional attunement, and integration of psychological principles. Students may be especially prone to overconfidence in domains where tacit knowledge and nuanced reasoning are essential, consistent with prior research on metacognitive miscalibration in non-procedural knowledge domains(Lombrozo, 2006 ). Collectively, these results underscore that perceived understanding is an unreliable guide to clinical competence, while higher-quality explanations are meaningfully linked with better diagnostic accuracy. This distinction has significant implications for curriculum design, assessment strategy, and metacognitive training in dental education. Our findings are consistent with the broader cognitive literature showing that people often mistake retrieval fluency for genuine understanding, a phenomenon well described in the new theory of disuse (Bjork & Bjork, 2011 ). Limitations This study has several limitations that should be considered when interpreting the findings. First, although the sample size was adequate and drawn from two academic cohorts, the study was conducted at a single institution, which may limit generalizability to other curricular contexts or dental schools with different pedagogical approaches. Second, explanatory tasks were written rather than oral; while written explanations provide a stable metric of explanatory depth, they may underestimate abilities of students who articulate more effectively verbally. However, written tasks are widely used in IOED research due to their reliability and standardization(Mills & Keil, 2004 ). Third, the scoring rubric although based on established explanatory frameworks relies on expert judgment. Although inter-rater reliability was excellent, subjective variation cannot be entirely eliminated. Fourth, diagnostic accuracy was assessed using short written vignettes rather than performance-based OSCE stations. While vignettes are validated for assessing clinical reasoning, they cannot capture all nuances of real-time pediatric decision-making. Finally, the study did not control for potential confounders such as prior pediatric clinical exposure, anxiety, or learning strategies, which may influence both explanatory ability and diagnostic accuracy. Despite these limitations, the convergence of findings across multiple measures (PEUS, OEPS, ECI, DAS) strengthens the validity of the conclusions. Implications for Dental Education The findings carry several important implications for improving pediatric dental education. Integrate explanation-based assessments into clinical teaching. Since explanatory performance predicted diagnostic accuracy, structured explanation tasks should be incorporated into seminars, case-based discussions, and clinical assessments to reveal conceptual gaps early. Teach metacognitive calibration explicitly. Students need guidance to recognize the limits of their understanding. Embedding short IOED exercises “rate, explain, reflect” can help recalibrate overestimation and strengthen awareness of knowledge gaps. Target behaviour guidance training. Given that behaviour guidance showed the largest IOED, curricula should include more authentic simulation, guided reflection, and deliberate practice in explaining behavioural management rationales. Use ECI as a formative diagnostic tool. The Explanatory Calibration Index may help educators identify students at risk of overconfidence-related reasoning errors, enabling targeted remediation or coaching. Shift from knowledge reassurance to explanatory depth. Many students feel confident because they recognize terminology; however, recognition does not equate understanding. Educational strategies should emphasize causal mechanisms, reasoning pathways, and structured justification of decisions. Strengthen feedback on explanation quality. Feedback should move beyond correctness toward evaluating clarity, completeness, and causal coherence of student explanations-components strongly linked with clinical reasoning. By systematically addressing IOED within dental education, programs can improve students’ metacognitive accuracy, enhance clinical reasoning, and ultimately support safer and more competent pediatric dental practice. Conclusion This study reveals a critical metacognitive blind spot in pediatric dental education: students believe they understand far more than they can explain. The pronounced discrepancy between perceived and actual explanatory depth-and its direct link to diagnostic inaccuracy-shows that the Illusion of Explanatory Depth is not a minor cognitive bias but a meaningful educational risk factor. Explanatory performance, not self-confidence, emerged as the true predictor of clinical reasoning. To overcome this gap, dental curricula must shift from passive recognition-based learning to explanation-centered, calibration-driven training, in which students routinely articulate, test, and refine their reasoning. Embedding structured explanation tasks, metacognitive reflection, and targeted feedback into pediatric dentistry can realign students’ perceptions with their actual understanding. When learners are taught to recognize the limits of what they know-and to explain before they conclude-they become safer, more accurate, and more effective clinicians. Declarations Ethics Approval and Standards Observed All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the University Research Ethics Committee and with the 2013 revision of the Declaration of Helsinki (World Medical Association). Ethical approval was obtained from the Institutional Review Board (IRB) (Ref No.: IRB/COD/FAC/12/April-2023). Informed consent was obtained from all participants. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors Acknowledgments The authors report that no external funding, financial support, or institutional resources were received for this study. No additional individuals or organizations contributed to the work in a manner requiring formal acknowledgment. Conflict of Interest Statement The authors declare no financial, academic, or personal conflicts of interest related to this study. All authors have reviewed and approved the final version of the manuscript. References Alter, A. L., & Oppenheimer, D. M. (2009). Uniting the tribes of fluency to form a metacognitive nation. Personality and Social Psychology Review , 13 (3), 219–235. Artino, A. R., Dong, T., DeZee, K. J., Gilliland, W. R., Waechter, D. M., Cruess, D., & Durning, S. J. (2012). 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Page, G., & Bordage, G. (1995). The Medical Council of Canada’s key features project: A more valid written examination of clinical decision-making skills. Academic Medicine , 70 (2), 104–110. Rozenblit, L., & Keil, F. (2002). The misunderstood limits of folk science: An illusion of explanatory depth. Cognitive Science , 26 (5), 521–562. Schuwirth, L. W. T., & van der Vleuten, C. P. M. (2004). Challenging the psychometric paradigm: Conceptualising assessment in medical education. Medical Education , 38 (4), 460–466. Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin , 86 (2), 420–428. Veenman, M. V. J. (2011). Learning to self-monitor and self-regulate. In R. E. Mayer, & P. A. Alexander (Eds.), Handbook of Research on Learning and Instruction (pp. 197–218). Routledge. Additional Declarations No competing interests reported. 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Surdilovic","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYJACiYQCBgY2Bv4PBh+APDZ2orQYgLQwGBTOAGlhJkYLgwGYNvjMA6IIaZGfkfvwxgMDm8Q+9gOJm21+bZPnY2Zg/PAxB7cWgxvpxhYJBmmJbTwJh41z+24btjEzMEvO3IZHi0QaG9Avh43ZJBjbjHN7bjMCtbAx8+LRIj8DroWZ/bdlz217gloYbkC0yLFJsDEYM/y4nUhQi8GZZ8wgv8ix8eQwGPY23E5uY2ZsxusX+fY0xps/Kmx45NvPMBj8+HPbdn5788EPH/E5DAUwtoHJBmLVg8AfUhSPglEwCkbBSAEAmFpHAPbZjSQAAAAASUVORK5CYII=","orcid":"","institution":"Ras al-Khaimah Medical and Health Sciences University","correspondingAuthor":true,"prefix":"","firstName":"Dusan","middleName":"","lastName":"Surdilovic","suffix":""},{"id":555513769,"identity":"0b4b9188-b8e5-4110-836d-dc4a2e8bbab5","order_by":1,"name":"Vivek Padmanabhan","email":"","orcid":"","institution":"Ras al-Khaimah Medical and Health Sciences University","correspondingAuthor":false,"prefix":"","firstName":"Vivek","middleName":"","lastName":"Padmanabhan","suffix":""},{"id":555513770,"identity":"2adab982-77c3-4de3-9e6f-f221cf45c0db","order_by":2,"name":"Md Sofiqul Islam","email":"","orcid":"","institution":"Ras al-Khaimah Medical and Health Sciences University","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Sofiqul","lastName":"Islam","suffix":""},{"id":555513772,"identity":"dc64e921-144b-4826-9d48-7614320729a0","order_by":3,"name":"Muhammed Mustahsen Rahman","email":"","orcid":"","institution":"Ras al-Khaimah Medical and Health Sciences University","correspondingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"Mustahsen","lastName":"Rahman","suffix":""}],"badges":[],"createdAt":"2025-12-02 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15:44:57","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86669,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8262006/v1/e217b286193ee0dd4fe56523.html"},{"id":97899585,"identity":"b229d5b8-fae9-41c1-9f4b-ce0810c120c5","added_by":"auto","created_at":"2025-12-10 15:44:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62741,"visible":true,"origin":"","legend":"\u003cp\u003eMean perceived explanatory understanding (PEUS) and scaled observed explanatory performance (OEPS × 2) across three pediatric dentistry topics.\u003c/p\u003e\n\u003cp\u003eError bars represent standard deviations. Across all topics, PEUS scores exceeded scaled OEPS scores, indicating a consistent Illusion of Explanatory Depth among final-year dental students. The discrepancy was largest for behaviour guidance.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8262006/v1/bb379703ceabf3ac4149b945.png"},{"id":97882988,"identity":"17a1767f-a4f4-424e-bb4c-2a7ea8606259","added_by":"auto","created_at":"2025-12-10 12:46:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67598,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of global Explanatory Calibration Index (ECI) among final-year dental students.\u003c/p\u003e\n\u003cp\u003ePositive ECI values reflect overestimation of explanatory depth (IOED), while negative values reflect underestimation. Most students demonstrated positive ECI values, indicating a general tendency toward overestimation.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8262006/v1/d79be69c195729468ebf6410.png"},{"id":97882990,"identity":"f46e6344-87f5-4dbd-bbc9-52d8b20799dd","added_by":"auto","created_at":"2025-12-10 12:46:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51583,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration categories across three pediatric dentistry topics.\u003c/p\u003e\n\u003cp\u003eBars represent percentages of students showing underestimation, accurate calibration, or overestimation (ECI-based categories). Overestimation was most prevalent in behaviour guidance, followed by pulp diagnosis and caries risk assessment.) across caries risk assessment, pulp diagnosis, and behaviour guidance.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8262006/v1/bcd75e267600f3d5b673484f.png"},{"id":97899904,"identity":"124fe1d9-37e8-4742-ae5d-aaa89d49e58c","added_by":"auto","created_at":"2025-12-10 15:45:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104116,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between explanatory measures and diagnostic accuracy.\u003c/p\u003e\n\u003cp\u003e(A) Scatter plot showing the relationship between observed explanatory performance (OEPS; 0–5 scale) and diagnostic accuracy score (DAS; 0–6 scale). Each point represents one student, with the fitted regression line indicating a positive association between explanatory quality and diagnostic accuracy.\u003c/p\u003e\n\u003cp\u003e(B) Scatter plot illustrating the association between the Explanatory Calibration Index (ECI) and diagnostic accuracy score (DAS). Higher ECI values reflect stronger overestimation of explanatory depth. The negative regression slope demonstrates that greater overestimation is associated with lower diagnostic accuracy.\u003c/p\u003e\n\u003cp\u003eTogether, the two panels indicate that actual explanatory ability (OEPS) - not perceived understanding - is meaningfully related to clinical diagnostic performance, and that stronger Illusion of Explanatory Depth (higher ECI) may impair diagnostic reasoning in pediatric dentistry.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8262006/v1/7ffba7d2bae903f05f9d3d24.png"},{"id":100549374,"identity":"bb64713e-bd0e-4a0c-872e-9479a48c64f0","added_by":"auto","created_at":"2026-01-19 08:23:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":794994,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8262006/v1/ffe59f6c-4bc8-43ec-83b1-6d54243f5fee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How Deep Is Their Understanding? The Illusion of Explanatory Depth in Pediatric Dentistry Training","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHigh-quality dental education increasingly relies on students\u0026rsquo; ability not only to acquire knowledge but also to accurately evaluate the depth of their own understanding-an essential component of metacognition. In clinical fields such as pediatric dentistry, where diagnostic reasoning, behavior management, and and treatment planning require layered causal understanding, a learner\u0026rsquo;s ability to judge what they truly understand directly affects clinical accuracy, patient safety, and the development of professional judgment. However, substantial evidence suggests that learners commonly hold inflated beliefs about the depth of their understanding, particularly in domains involving complex mechanisms. This bias is known as the Illusion of Explanatory Depth (IOED), first described by Rozenblit and Keil, who demonstrated that people systematically overestimate how well they can explain causal systems once they attempt to articulate them in detail(Rozenblit \u0026amp; Keil, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMetacognitive accuracy plays a crucial role in health professions education. Students who can accurately judge their understanding tend to make better clinical decisions, seek information more efficiently, and avoid premature diagnostic closure(Eva \u0026amp; Regehr, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In medicine, studies show that diagnostic precision improves when learners recognize limits in their understanding(Friedman et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In dentistry, similar evidence indicates that miscalibrated judgments-where perceived competence exceeds actual performance-can negatively affect treatment planning and patient outcomes(Chaffin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). These observations align with broader cognitive research demonstrating that individuals often rely on superficial cues such as familiarity or fluency when estimating their own understanding, rather than true conceptual mastery(Alter \u0026amp; Oppenheimer, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMost dental-education research examining metacognition has focused on self-assessment, calibration of confidence, or general overconfidence bias. A substantial body of work has documented that dental students frequently misjudge their competence during preclinical and clinical tasks(Manogue \u0026amp; Brown, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In addition, evidence from assessment research shows that global self-evaluations rarely correlate strongly with objective performance measures(Schuwirth \u0026amp; van der Vleuten, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). While these constructs are informative, they differ from IOED. Overconfidence reflects a mismatch between confidence and correctness; self-assessment captures broad perceptions of competence. IOED, however, specifically explores the discrepancy between perceived explanatory depth and actual ability to generate accurate, coherent explanations.\u003c/p\u003e\u003cp\u003ePediatric dentistry offers a rich context for exploring IOED. Topics such as caries risk assessment, pulp diagnosis in children, space management, or behavior guidance require students to integrate biological, developmental, and behavioral principles. Yet, research shows that students often display confidence levels that surpass their diagnostic accuracy in pediatric clinical scenarios(Divaris et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Additional studies identify systematic errors in clinical judgment among dental students managing pediatric cases(Moura et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Such findings suggest that IOED may be an underlying cognitive mechanism contributing to these discrepancies.\u003c/p\u003e\u003cp\u003eFurther, contemporary dental curricula increasingly emphasize active learning and case-based strategies intended to foster deeper understanding. Yet empirical evidence shows that even after interactive learning experiences, students may feel they understand a topic more deeply than they can actually explain it(Michael, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Psychological research indicates that generating explanations is one of the most sensitive methods for revealing gaps in understanding-far more effective than recognition-based tasks(Lombrozo, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This makes IOED particularly relevant for understanding how students internalize, structure, and apply knowledge in pediatric dentistry.\u003c/p\u003e\u003cp\u003eMetacognition also influences clinical competency performance. In medical education, students demonstrating stronger metacognitive regulation tend to perform better on OSCEs and exhibit more accurate diagnostic reasoning(Artino et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Conversely, diagnostic error research identifies overestimation of understanding as a contributor to reasoning shortcuts and premature closure(Berner \u0026amp; Graber, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These insights are highly relevant to pediatric dentistry, where decisions must account for child behavior, developmental considerations, and the modification of adult-based diagnostic frameworks.\u003c/p\u003e\u003cp\u003eDespite the importance of explanatory accuracy in clinical decision-making, IOED remains unexplored in dental education. No published study has systematically examined the discrepancy between perceived and actual explanatory depth among dental students, particularly within a pediatric context. Addressing this gap is essential because pediatric dentistry requires students to apply complex reasoning to real-world clinical problems involving children, where misunderstanding can directly influence treatment quality.\u003c/p\u003e\u003cp\u003eTherefore, the present study aims to evaluate the Illusion of Explanatory Depth among final-year dental students by comparing their self-perceived understanding with their demonstrated explanatory accuracy across core pediatric dentistry concepts. By quantifying the magnitude of IOED and identifying its association with explanatory performance, this study seeks to reveal metacognitive vulnerabilities and inform targeted improvements in pediatric dentistry training.\u003c/p\u003e\n\u003ch3\u003eAim of the study\u003c/h3\u003e\n\u003cp\u003eThe aim of this study was to investigate the Illusion of Explanatory Depth (IOED) among final-year dental students in pediatric dentistry by (i) comparing their self-rated explanatory understanding of core pediatric topics with their objectively rated explanatory performance, (ii) quantifying the calibration gap between perceived and actual explanatory depth, (iii) evaluating topic-specific differences across caries risk assessment, pulp diagnosis, and behaviour guidance, and (iv) examining the association between explanatory depth and diagnostic accuracy in pediatric clinical scenarios.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eStudy design and context\u003c/p\u003e\u003cp\u003eThis study employed a cross-sectional explanatory design integrating self-assessed understanding, structured explanatory tasks, calibration analysis, and diagnostic accuracy testing. Multi-component metacognitive designs of this type are recommended when investigating discrepancies between perceived and actual understanding in health professions education, because they allow simultaneous examination of subjective judgments, explanatory performance, and applied clinical reasoning(Veenman, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study was conducted in the Department of Pediatric Dentistry at a dental school in the Middle East during two consecutive academic years, providing a stable curricular context and comparable instructional exposure for all participating students.\u003c/p\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003eThe target population comprised final-year (5th year) Bachelor of Dental Surgery (BDS) students enrolled in the pediatric dentistry clinical course. Across the two academic years, 148 students were eligible to participate.\u003c/p\u003e\u003cp\u003eInclusion criteria were:\u003c/p\u003e\u003cp\u003eactive enrollment in the pediatric dentistry course,\u003c/p\u003e\u003cp\u003ecompletion of all core theoretical modules related to pediatric caries, pulp therapy in primary teeth, and behaviour guidance,\u003c/p\u003e\u003cp\u003eno ongoing remedial status or prolonged absence from clinical duties.\u003c/p\u003e\u003cp\u003e Participation was voluntary, and all students provided written informed consent.\u003c/p\u003e\u003cp\u003eSample sizes exceeding approximately 100 participants are considered adequate for stable estimation of metacognitive indices and reliability of performance-based measures in educational research(Kline, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSelection of pediatric topics\u003c/p\u003e\u003cp\u003eThree core pediatric domains were selected as IOED targets:\u003c/p\u003e\u003cp\u003eCaries risk assessment in children\u003c/p\u003e\u003cp\u003ePulp diagnosis and management in primary teeth\u003c/p\u003e\u003cp\u003eBehaviour guidance for anxious pediatric patients\u003c/p\u003e\u003cp\u003eThese domains were chosen because they require integration of biological, developmental, and behavioural concepts, and previous work has shown that dental students frequently display errors or inconsistencies in clinical judgment specifically in pediatric scenarios(Moura et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The topics align with the existing pediatric dentistry curriculum, ensuring content familiarity for all final-year students.\u003c/p\u003e\u003cp\u003eMeasures\u003c/p\u003e\u003cp\u003e1. Perceived Explanatory Understanding Score (PEUS)\u003c/p\u003e\u003cp\u003ePEUS captured students’ self-rated explanatory understanding prior to any explanatory or diagnostic task. For each of the three topics, students were asked:\u003c/p\u003e\u003cp\u003eHow well do you believe you can explain this concept to a peer?\u003c/p\u003e\u003cp\u003eEach response was recorded on a 10-point numerical rating scale (1 = “not at all well”, 10 = “extremely well”). Single-item metacognitive judgments of this type are widely used as global indicators of perceived understanding and have been shown to be informative for calibration research(Dunlosky \u0026amp; Metcalfe, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePEUS values were obtained for each topic separately (range 1–10). A global PEUS was computed as the arithmetic mean of the three topic-specific scores.\u003c/p\u003e\u003cp\u003e2. Observed Explanatory Performance Score (OEPS)\u003c/p\u003e\u003cp\u003eOEPS represented students’ objectively rated explanatory ability. Following the canonical Illusion of Explanatory Depth (IOED) paradigm[1,18], students were asked to produce short written explanations for each of the three pediatric topics.\u003c/p\u003e\u003cp\u003eExplanatory prompts\u003c/p\u003e\u003cp\u003eStudents responded in writing (4–6 sentences) to the following prompts:\u003c/p\u003e\u003cp\u003eExplain the process of caries risk assessment in a 6-year-old child.\u003c/p\u003e\u003cp\u003eExplain the diagnostic approach and treatment decision-making for pulp involvement in a primary molar.\u003c/p\u003e\u003cp\u003eExplain the steps of behaviour guidance for an anxious child attending the dental clinic for the first time.\u003c/p\u003e\u003cp\u003eThese prompts were designed to elicit causal, conceptual, and procedural knowledge, consistent with prior research demonstrating that explanation tasks are highly sensitive to gaps in understanding(Chi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eScoring rubric and rating procedure\u003c/p\u003e\u003cp\u003eTwo pediatric dentistry faculty members independently rated each explanation using a 5-point analytic rubric, adapted from explanatory assessment frameworks in cognitive and educational research[19,20]. The rubric evaluated:\u003c/p\u003e\u003cp\u003eCausal accuracy (correctness of key mechanisms and relationships),\u003c/p\u003e\u003cp\u003eConceptual completeness (coverage of essential elements),\u003c/p\u003e\u003cp\u003eLogical structure (clarity and coherence of reasoning),\u003c/p\u003e\u003cp\u003eClinical relevance (appropriateness for pediatric practice).\u003c/p\u003e\u003cp\u003eEach dimension was scored from 0 to 5, and an overall OEPS score (0–5) per topic was assigned based on holistic integration of these criteria, following established practices in performance-based explanation scoring(Lombrozo, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInter-rater reliability for OEPS was evaluated using a two-way random intraclass correlation coefficient (ICC), which is recommended for reliability studies involving multiple raters and continuous scores(Shrout \u0026amp; Fleiss, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Discrepant ratings were resolved by discussion, but only scores from the independent ratings were used for ICC computation.\u003c/p\u003e\u003cp\u003eA global OEPS was calculated as the mean of the three topic-specific scores.\u003c/p\u003e\u003cp\u003e3. Explanatory Calibration Index (ECI)\u003c/p\u003e\u003cp\u003eThe Explanatory Calibration Index (ECI) quantified the gap between perceived and actual explanatory depth, providing a direct operationalization of IOED. Calibration metrics similar to ECI have a long tradition in judgment and decision-making research and are widely used to assess metacognitive accuracy(Lichtenstein et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1982\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBecause PEUS was recorded on a 1–10 scale and OEPS on a 0–5 scale, OEPS values were first linearly transformed to a 0–10 metric by multiplication with two. For each topic, ECI was computed as:\u003c/p\u003e\n\u003ch3\u003eECI = PEUS – (OEPS × 2)\u003c/h3\u003e\n\u003cp\u003eInterpretation:\u003c/p\u003e\u003cp\u003eECI\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;2 \u0026rarr; overestimation (strong IOED pattern),\u003c/p\u003e\u003cp\u003eECI between \u0026minus;\u0026thinsp;1 and +\u0026thinsp;1 \u0026rarr; good calibration,\u003c/p\u003e\u003cp\u003eECI \u0026lt; \u0026minus;\u0026thinsp;2 \u0026rarr; underestimation (impostor-like pattern).\u003c/p\u003e\u003cp\u003eECI was calculated per topic and as a global index (mean of three ECI values). This allowed analysis of both topic-specific and overall metacognitive calibration.\u003c/p\u003e\u003cp\u003e4. Diagnostic Accuracy Score (DAS)\u003c/p\u003e\u003cp\u003eDAS measured applied pediatric diagnostic accuracy, independent of self-perception or explanatory performance. Diagnostic accuracy tests are recognized as valid indicators of clinical reasoning quality in health professions education(Eva, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudents completed six pediatric case-based micro-vignettes, constructed in alignment with key-feature format principles(Page \u0026amp; Bordage, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1995\u003c/span\u003e):\u003c/p\u003e\u003cp\u003e2 vignettes focused on caries risk assessment,\u003c/p\u003e\u003cp\u003e2 on pulp diagnosis and management in primary teeth,\u003c/p\u003e\u003cp\u003e2 on behaviour guidance in common pediatric scenarios.\u003c/p\u003e\u003cp\u003eEach vignette was followed by a single best-answer question targeting the critical diagnostic or management decision. Responses were scored 0 (incorrect) or 1 (correct), yielding a total DAS range from 0 to 6. Higher scores indicated better diagnostic accuracy.\u003c/p\u003e\u003cp\u003eData collection procedure\u003c/p\u003e\u003cp\u003eData collection followed a fixed sequence, closely aligned with established IOED protocols[1,18]:\u003c/p\u003e\u003cp\u003ePEUS ratings: students first rated their perceived explanatory understanding for each of the three topics.\u003c/p\u003e\u003cp\u003eExplanatory tasks: students then wrote 4\u0026ndash;6 sentence explanations for each topic, without access to notes or electronic resources.\u003c/p\u003e\u003cp\u003eOEPS scoring: explanations were subsequently rated independently by two faculty members using the analytic rubric.\u003c/p\u003e\u003cp\u003eDiagnostic vignettes: students completed the six pediatric micro-vignettes (DAS).\u003c/p\u003e\u003cp\u003eAll activities were conducted in a supervised classroom environment, during a single scheduled session, to standardize conditions and prevent consultation of external resources. The total time required to complete all components was approximately 35\u0026ndash;40 minutes, which is comparable to durations reported in explanation-based metacognitive studies and educational assessment sessions(Lodge \u0026amp; Kennedy, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEthical considerations\u003c/p\u003e\u003cp\u003e The study protocol was reviewed and approved by the institutional research ethics committee. Participation was voluntary, and students were informed that they could withdraw at any time without academic consequences. Data were anonymized prior to analysis; individual scores were not shared with course instructors in a way that could influence summative assessment. These procedures are consistent with best-practice recommendations for ethics in educational research involving students(Kass et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOverview of analyses\u003c/p\u003e\u003cp\u003eAll analyses were conducted at the student level. Descriptive statistics were calculated for all primary variables: PEUS, OEPS, ECI, and DAS. Paired-samples tests were used to compare perceived explanatory understanding (PEUS) with scaled explanatory performance (OEPS\u0026times;2) for each topic. Repeated-measures analyses examined topic-specific differences in IOED indicators. Calibration categories were derived from the ECI thresholds, and their distribution was summarized descriptively. Associations among explanatory measures and diagnostic accuracy were explored using Pearson\u0026rsquo;s correlation coefficients. A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was adopted for all tests.\u003c/p\u003e\u003cp\u003eParticipant characteristics\u003c/p\u003e\u003cp\u003eOf the 148 eligible final-year students, 142 (95.9%) completed all components of the study and were included in the analysis. The sample had a mean age of 23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 years, and 62.0% of participants were female. The mean cumulative GPA was 3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39 (on a 4-point scale). Baseline characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipant characteristics (N\u0026thinsp;=\u0026thinsp;142)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88 (62.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (38.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative GPA (0\u0026ndash;4), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademic year 1, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademic year 2, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eInter-rater reliability for the OEPS ratings was high; the two-way random ICC for the overall explanatory score across all topics was 0.86 (95% CI 0.81\u0026ndash;0.90), indicating excellent agreement between raters.\u003c/p\u003e\u003cp\u003eDescriptive statistics for PEUS, OEPS, ECI and DAS\u003c/p\u003e\u003cp\u003eAcross the three pediatric topics, students reported relatively high levels of perceived explanatory understanding. Mean PEUS scores ranged from 7.2 to 8.1 on a 10-point scale. In contrast, observed explanatory performance (OEPS) mean scores ranged from 2.8 to 3.2 on the 0\u0026ndash;5 scale. After rescaling OEPS to a 0\u0026ndash;10 range, the mean global Explanatory Calibration Index (ECI) was +\u0026thinsp;1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5, indicating a general tendency toward overestimation.\u003c/p\u003e\u003cp\u003eThe mean Diagnostic Accuracy Score (DAS) was 4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 out of 6, with scores spanning the full range from 0 to 6. Descriptive statistics for all variables by topic and globally are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics for PEUS, OEPS, ECI and DAS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTopic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaries risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePulp diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEUS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBehaviour guidance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEUS (global)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean of 3 topics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOEPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaries risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOEPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePulp diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOEPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBehaviour guidance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOEPS (global)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean of 3 topics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaries risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eapprox \u0026minus;\u0026thinsp;5 to +\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePulp diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eapprox \u0026minus;\u0026thinsp;5 to +\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBehaviour guidance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eapprox \u0026minus;\u0026thinsp;5 to +\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECI (global)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean of 3 topics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eapprox \u0026minus;\u0026thinsp;5 to +\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll vignettes (6 items)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTopic-specific IOED patterns\u003c/p\u003e\u003cp\u003ePaired comparisons between PEUS and scaled OEPS demonstrated a consistent Illusion of Explanatory Depth across all three topics. For caries risk assessment, the mean PEUS was 7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2, whereas the scaled OEPS (OEPS\u0026times;2) was 6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5, yielding a significant mean difference of 1.4 points (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For pulp diagnosis and management, PEUS was 7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 and scaled OEPS 5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6, a difference of 1.6 points (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The largest discrepancy was observed for behaviour guidance, where PEUS reached 8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1, while scaled OEPS was 6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7, corresponding to a mean difference of 2.1 points (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eGlobal comparisons across topics using repeated-measures analysis indicated significant variation in calibration, with the highest ECI values observed in behaviour guidance. Post-hoc comparisons confirmed that behaviour guidance ECI was significantly higher than ECI for caries risk assessment and pulp diagnosis (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These patterns are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which compares mean PEUS and scaled OEPS for each topic.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eError bars represent standard deviations. Across all topics, PEUS scores exceeded scaled OEPS scores, indicating a consistent Illusion of Explanatory Depth among final-year dental students. The discrepancy was largest for behaviour guidance.\u003c/p\u003e\u003cp\u003eDistribution of calibration categories:\u003c/p\u003e\u003cp\u003eBased on the ECI thresholds, students were categorized as:\u003c/p\u003e\u003cp\u003eWell calibrated (ECI between \u0026minus;\u0026thinsp;1 and +\u0026thinsp;1)\u003c/p\u003e\u003cp\u003eOverestimators (ECI\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;2)\u003c/p\u003e\u003cp\u003eUnderestimators (ECI \u0026lt; \u0026minus;\u0026thinsp;2)\u003c/p\u003e\u003cp\u003eFor the global ECI, 58.5% of students fell into the overestimation category, 32.4% were well calibrated, and 9.2% underestimated their explanatory depth. Topic-specific distributions showed that overestimation was most prevalent in behaviour guidance, where 68.3% of students met the overestimation criterion, compared with 51.4% for caries risk assessment and 55.6% for pulp diagnosis.\u003c/p\u003e\u003cp\u003eThe distribution of global ECI values is presented as a density/histogram in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and topic-specific calibration categories are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePositive ECI values reflect overestimation of explanatory depth (IOED), while negative values reflect underestimation. Most students demonstrated positive ECI values, indicating a general tendency toward overestimation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBars represent percentages of students showing underestimation, accurate calibration, or overestimation (ECI-based categories). Overestimation was most prevalent in behaviour guidance, followed by pulp diagnosis and caries risk assessment.) across caries risk assessment, pulp diagnosis, and behaviour guidance.\u003c/p\u003e\u003cp\u003eThese findings indicate that while a minority of students demonstrated accurate self-calibration, the majority displayed a systematic Illusion of Explanatory Depth, particularly in behaviour guidance scenarios.\u003c/p\u003e\u003cp\u003eAssociations between explanatory measures and diagnostic accuracy\u003c/p\u003e\u003cp\u003eCorrelation analyses revealed differential relationships among perceived understanding, explanatory performance, calibration, and diagnostic accuracy.\u003c/p\u003e\u003cp\u003eThere was a small but significant positive correlation between PEUS and OEPS (r\u0026thinsp;=\u0026thinsp;0.22, p\u0026thinsp;=\u0026thinsp;0.008), suggesting that students who perceived themselves as better explainers tended, to some extent, to explain more accurately.\u003c/p\u003e\u003cp\u003ePEUS and DAS were not significantly correlated (r\u0026thinsp;=\u0026thinsp;0.12, p\u0026thinsp;=\u0026thinsp;0.14), indicating that higher perceived explanatory understanding did not reliably predict better diagnostic accuracy.\u003c/p\u003e\u003cp\u003eIn contrast, OEPS showed a moderate positive correlation with DAS (r\u0026thinsp;=\u0026thinsp;0.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that students who provided higher-quality explanations also performed better on pediatric diagnostic vignettes.\u003c/p\u003e\u003cp\u003eThe ECI was negatively correlated with DAS (r = \u0026minus;\u0026thinsp;0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that greater overestimation of explanatory depth was associated with lower diagnostic accuracy.\u003c/p\u003e\u003cp\u003eThese relationships are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, which displays scatter plots of OEPS versus DAS and ECI versus DAS with regression lines.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelations among PEUS, OEPS, ECI and DAS (N\u0026thinsp;=\u0026thinsp;142)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEUS (global) \u0026ndash; OEPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEUS (global) \u0026ndash; DAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOEPS (global) \u0026ndash; DAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECI (global) \u0026ndash; DAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e(Pearson’s correlation coefficients.)\u003c/h3\u003e\n\u003cp\u003eTogether, the two panels indicate that actual explanatory ability (OEPS) - not perceived understanding - is meaningfully related to clinical diagnostic performance, and that stronger Illusion of Explanatory Depth (higher ECI) may impair diagnostic reasoning in pediatric dentistry.\u003c/p\u003e\u003cp\u003eCollectively, these results suggest that actual explanatory depth, rather than perceived understanding, is meaningfully associated with clinical diagnostic performance, and that stronger IOED (higher ECI) may represent a metacognitive risk factor for reduced diagnostic accuracy in pediatric dentistry.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the Illusion of Explanatory Depth (IOED) among final-year dental students by comparing self-rated explanatory understanding with objectively evaluated explanatory performance and diagnostic accuracy in pediatric dentistry. Across all three core pediatric topics-caries risk assessment, pulp diagnosis, and behaviour guidance-students consistently overestimated the depth of their understanding, with the largest gap observed in behaviour guidance. This pattern mirrors the central premise of IOED theory: individuals believe they understand complex systems more deeply than they actually do once they attempt to explain them in detail(Rozenblit \u0026amp; Keil, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe finding that perceived understanding (PEUS) was high across all topics yet showed only a weak association with diagnostic performance suggests that students\u0026rsquo; subjective judgments are poor indicators of true conceptual mastery. IOED appears to function as a cognitive blind spot in pediatric dental training, particularly in domains requiring integration of behavioural and procedural reasoning. This aligns with evidence from cognitive psychology showing that learners often rely on feelings of familiarity or fluency rather than actual explanatory depth when making metacognitive judgments(Alter \u0026amp; Oppenheimer, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, observed explanatory performance (OEPS) demonstrated a moderate positive association with diagnostic accuracy. This reinforces the central role of explanation quality as a cognitive marker of understanding. Explanation-based assessments have been shown to reveal conceptual gaps that recognition-based tasks fail to detect(Chi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). In the current study, students who articulated clearer, more coherent explanations were also the ones who performed better on pediatric diagnostic scenarios.\u003c/p\u003e\u003cp\u003eThe Explanatory Calibration Index (ECI) provided strong evidence of metacognitive misalignment. More than half of the students demonstrated overestimation, and higher ECI values were associated with lower diagnostic accuracy. This inverse relationship suggests that IOED is not a benign cognitive quirk but may represent a metacognitive risk factor that undermines clinical reasoning. Similar patterns have been reported in medical diagnostic error literature, where overestimation contributes to premature closure and misjudgment(Berner \u0026amp; Graber, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The current findings extend this phenomenon to pediatric dental education, highlighting the potential consequences of poorly calibrated explanatory understanding.\u003c/p\u003e\u003cp\u003eTopic-specific differences offer further insight. Behaviour guidance consistently showed the largest IOED magnitude. This may reflect the inherently complex and less linear nature of behavioural reasoning in pediatric clinical practice, which requires situational judgment, emotional attunement, and integration of psychological principles. Students may be especially prone to overconfidence in domains where tacit knowledge and nuanced reasoning are essential, consistent with prior research on metacognitive miscalibration in non-procedural knowledge domains(Lombrozo, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCollectively, these results underscore that perceived understanding is an unreliable guide to clinical competence, while higher-quality explanations are meaningfully linked with better diagnostic accuracy. This distinction has significant implications for curriculum design, assessment strategy, and metacognitive training in dental education. Our findings are consistent with the broader cognitive literature showing that people often mistake retrieval fluency for genuine understanding, a phenomenon well described in the new theory of disuse (Bjork \u0026amp; Bjork, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLimitations\u003c/p\u003e\u003cp\u003eThis study has several limitations that should be considered when interpreting the findings.\u003c/p\u003e\u003cp\u003eFirst, although the sample size was adequate and drawn from two academic cohorts, the study was conducted at a single institution, which may limit generalizability to other curricular contexts or dental schools with different pedagogical approaches.\u003c/p\u003e\u003cp\u003e Second, explanatory tasks were written rather than oral; while written explanations provide a stable metric of explanatory depth, they may underestimate abilities of students who articulate more effectively verbally. However, written tasks are widely used in IOED research due to their reliability and standardization(Mills \u0026amp; Keil, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThird, the scoring rubric although based on established explanatory frameworks relies on expert judgment. Although inter-rater reliability was excellent, subjective variation cannot be entirely eliminated.\u003c/p\u003e\u003cp\u003eFourth, diagnostic accuracy was assessed using short written vignettes rather than performance-based OSCE stations. While vignettes are validated for assessing clinical reasoning, they cannot capture all nuances of real-time pediatric decision-making.\u003c/p\u003e\u003cp\u003eFinally, the study did not control for potential confounders such as prior pediatric clinical exposure, anxiety, or learning strategies, which may influence both explanatory ability and diagnostic accuracy.\u003c/p\u003e\u003cp\u003eDespite these limitations, the convergence of findings across multiple measures (PEUS, OEPS, ECI, DAS) strengthens the validity of the conclusions.\u003c/p\u003e\u003cp\u003eImplications for Dental Education\u003c/p\u003e\u003cp\u003eThe findings carry several important implications for improving pediatric dental education.\u003c/p\u003e\u003cp\u003eIntegrate explanation-based assessments into clinical teaching.\u003c/p\u003e\u003cp\u003eSince explanatory performance predicted diagnostic accuracy, structured explanation tasks should be incorporated into seminars, case-based discussions, and clinical assessments to reveal conceptual gaps early.\u003c/p\u003e\u003cp\u003eTeach metacognitive calibration explicitly.\u003c/p\u003e\u003cp\u003eStudents need guidance to recognize the limits of their understanding. Embedding short IOED exercises \u0026ldquo;rate, explain, reflect\u0026rdquo; can help recalibrate overestimation and strengthen awareness of knowledge gaps.\u003c/p\u003e\u003cp\u003eTarget behaviour guidance training.\u003c/p\u003e\u003cp\u003eGiven that behaviour guidance showed the largest IOED, curricula should include more authentic simulation, guided reflection, and deliberate practice in explaining behavioural management rationales.\u003c/p\u003e\u003cp\u003eUse ECI as a formative diagnostic tool.\u003c/p\u003e\u003cp\u003eThe Explanatory Calibration Index may help educators identify students at risk of overconfidence-related reasoning errors, enabling targeted remediation or coaching.\u003c/p\u003e\u003cp\u003eShift from knowledge reassurance to explanatory depth.\u003c/p\u003e\u003cp\u003eMany students feel confident because they recognize terminology; however, recognition does not equate understanding. Educational strategies should emphasize causal mechanisms, reasoning pathways, and structured justification of decisions.\u003c/p\u003e\u003cp\u003eStrengthen feedback on explanation quality.\u003c/p\u003e\u003cp\u003eFeedback should move beyond correctness toward evaluating clarity, completeness, and causal coherence of student explanations-components strongly linked with clinical reasoning.\u003c/p\u003e\u003cp\u003eBy systematically addressing IOED within dental education, programs can improve students\u0026rsquo; metacognitive accuracy, enhance clinical reasoning, and ultimately support safer and more competent pediatric dental practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals a critical metacognitive blind spot in pediatric dental education: students believe they understand far more than they can explain. The pronounced discrepancy between perceived and actual explanatory depth-and its direct link to diagnostic inaccuracy-shows that the Illusion of Explanatory Depth is not a minor cognitive bias but a meaningful educational risk factor. Explanatory performance, not self-confidence, emerged as the true predictor of clinical reasoning.\u003c/p\u003e\u003cp\u003eTo overcome this gap, dental curricula must shift from passive recognition-based learning to explanation-centered, calibration-driven training, in which students routinely articulate, test, and refine their reasoning. Embedding structured explanation tasks, metacognitive reflection, and targeted feedback into pediatric dentistry can realign students\u0026rsquo; perceptions with their actual understanding. When learners are taught to recognize the limits of what they know-and to explain before they conclude-they become safer, more accurate, and more effective clinicians.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Standards Observed\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the University Research Ethics Committee and with the 2013 revision of the Declaration of Helsinki (World Medical Association). Ethical approval was obtained from the Institutional Review Board (IRB) (Ref No.: IRB/COD/FAC/12/April-2023). Informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors\u003c/p\u003e\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors report that no external funding, financial support, or institutional resources were received for this study. No additional individuals or organizations contributed to the work in a manner requiring formal acknowledgment.\u003c/p\u003e\n\u003cp\u003eConflict of Interest Statement\u003c/p\u003e\n\u003cp\u003eThe authors declare no financial, academic, or personal conflicts of interest related to this study. All authors have reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlter, A. L., \u0026amp; Oppenheimer, D. M. (2009). 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Routledge.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Illusion of Explanatory Depth, Metacognition, Explanatory Accuracy, Diagnostic Reasoning, Calibration, Pediatric Dentistry Education, Clinical Decision-Making, Health Professions Education","lastPublishedDoi":"10.21203/rs.3.rs-8262006/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8262006/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePurpose: This study examined the Illusion of Explanatory Depth (IOED) among final-year dental students in pediatric dentistry by comparing perceived explanatory understanding with objectively evaluated explanatory performance and diagnostic accuracy. The goal was to determine whether IOED represents a metacognitive vulnerability that may contribute to diagnostic errors in pediatric dental decision-making.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: A cross-sectional explanatory design was used with 142 final-year Bachelor of Dental Surgery students across two academic years. Students rated their perceived explanatory understanding (PEUS) for three pediatric topics (caries risk assessment, pulp diagnosis, behaviour guidance). They then completed structured written explanations scored using a validated rubric to generate Observed Explanatory Performance Scores (OEPS). The Explanatory Calibration Index (ECI = PEUS - OEPS×2) quantified the degree of over- or underestimation. Diagnostic accuracy was assessed through six pediatric key-feature vignettes (DAS). Associations among PEUS, OEPS, ECI, and DAS were examined using paired comparisons and Pearson correlations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Students consistently overestimated their explanatory understanding across all topics, with the largest IOED effect observed in behaviour guidance. Mean global ECI indicated substantial overestimation. OEPS showed a moderate positive correlation with diagnostic accuracy (r = 0.47, p \u0026lt; .001), whereas PEUS did not (r = 0.12, p = .14). Higher ECI values were negatively associated with diagnostic accuracy (r = –0.31, p \u0026lt; .001), suggesting that greater overestimation was linked to poorer clinical reasoning performance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions: Dental students systematically overestimate the depth of their understanding in pediatric dentistry, and IOED appears to function as a meaningful metacognitive risk factor. Explanatory performance - not perceived understanding, was the reliable predictor of diagnostic accuracy. Incorporating structured explanation tasks, metacognitive calibration activities, and targeted feedback into pediatric dental education may help align students’ perceived and actual understanding, improving diagnostic performance and supporting safer clinical practice.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"How Deep Is Their Understanding? The Illusion of Explanatory Depth in Pediatric Dentistry Training","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-10 12:45:57","doi":"10.21203/rs.3.rs-8262006/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":"bb7b0666-3ef8-43f5-b659-0118ad769f2b","owner":[],"postedDate":"December 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-18T14:38:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-10 12:45:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8262006","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8262006","identity":"rs-8262006","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}