ArticleTurkish journal of orthodontics2026
Assessing the Diagnostic Performance of ChatGPT-5.0 versus Machine Learning in Orthodontics: A Comparative Analysis for Extraction Treatment Planning.
Article in Turkish journal of orthodontics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Severity Stratification Changes What Hallucination Rates Mean: An Item-Matched Audit of Five Large Language Models in Orthodontic Decision Support.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To make accurate orthodontic extraction decisions, various clinical and cephalometric variables must be evaluated. This study aims to evaluate ChatGPT-5.0's performance in distinguishing orthodontic extraction decisions and to compare it with five supervised machine learning (ML) algorithms. Methods: Of 550 retrospectively evaluated orthodontic records, 30 were reserved for calibration, leaving 520 for the main analysis. The reference standard was the consensus treatment decision of three expert orthodontists with more than 5 years of clinical experience. Overall, 23 variables were analyzed, including 13 clinical parameters, 7 cephalometric measurements, and photographs. ChatGPT-5.0's performance was evaluated using a 5-fold cross-validation design. It was compared with XGBoost, random forest, support vector machine (SVM), logistic regression, and multi-layer perceptron (MLP). Performance metrics included accuracy, sensitivity, specificity, precision, F1-score, and balanced accuracy, with 95% confidence intervals calculated. Statistical analyses utilized Cochran's Q test and the McNemar test with Holm-Bonferroni correction. Results: Of the 520 main cases, 223 (42.88%) were extraction treatments and 297 (57.12%) were non-extraction treatments. XGBoost achieved the highest accuracy (78.08%), followed closely by ChatGPT-5.0 (75.77%). The overall performance difference among models was significant (p≤0.001). In pairwise comparisons, ChatGPT's accuracy was significantly higher than those of random forest, SVM, logistic regression, and MLP, but was found to be similar to XGBoost. ChatGPT-5.0 showed the highest sensitivity (76.68%), whereas XGBoost showed the highest specificity (82.15%). Conclusion: ChatGPT-5.0 demonstrated performance comparable to, and in some cases superior to, traditional ML models for orthodontic extraction decisions. While XGBoost yielded the highest overall classification accuracy, ChatGPT-5.0's high sensitivity in detecting extraction cases was noteworthy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.