Orthodontic Treatment: Real Risk for Dental Age Estimation in Adults?
Bibliographic record
Abstract
Dental age estimation becomes a challenge once the root formation is concluded. In living adults, one dental age indicator is the formation of secondary dentine, also associated with orthodontic treatment as well as root shortening. The aim of this study was to establish whether these secondary effects of orthodontic treatment could generate a statistically significant difference in dental age estimations when using Kvaal's method. The study sample included 34 pairs of pre- and postorthodontic panoramic radiographs, from different individuals with exactly the same age and sex distribution. Females 65%, median age 17.5 years, and males 35%, median age 22.5 years, were included. After data collection, dental age was estimated per tooth using formulae previously published. The risk of obtaining over-estimation of age was calculated. (RR = 1.007). The changes caused by orthodontic treatment do not have any significant effect on age estimation when Kvaal et al.'s method is applied on panoramic radiographs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".