Definition of At-Risk Occlusal Surfaces of Permanent Molars—A Descriptive Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
UNLABELLED: THE OBJECTIVE of this descriptive study was to define the at-risk occlusal surface to guide the practitioner in the decision of whether to seal or not. METHOD: All dentists affiliated with the French Society of Pediatric Odontology (SFOP) and general practitioners (GP) registered in postgraduate courses in three French dental schools answered the same questionnaire illustrating four occlusal surfaces of permanent molars. It was focused on obtaining an optimal definition of an at-risk occlusal surface. The corresponding four molars were later sectioned to check the answers. Univariate logistic regression analyses and multivariate logistic regression models were tested to identify the factors associated with the at-risk occlusal surface. RESULTS: Eighty-six SFOP dentists and 136 GP filled in the form. Multivariate logistic regression models stratified by type of practice demonstrated that stained fissures (p =0.001) were only associated with at-risk occlusal surface among GP and the morphology of primary fissure (p=0.001) when considering SFOP dentists alone. The multivariate analyses demonstrated that stained fissures, and primary and secondary fissures were linked to the perception of an at-risk occlusal surface. CONCLUSION: An at-risk occlusal surface has narrow and deep primary fissures. Numerous secondary fissures could increase the risk. The coloration of fissures should not be used in the definition because it depends on tooth integrity.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it