The management of incipient or suspicious occlusal caries: a decision‐tree analysis
Bibliographic record
Abstract
OBJECTIVE: To perform a comprehensive decision-tree analysis for the management of the suspicious/incipient occlusal lesion on a molar tooth. METHODS: A quantitative decision tree was constructed to assess the expected utility value of three global strategies to dentally manage the incipient or suspicious occlusal carious lesion. RESULT: A preventive strategy offered an optimal expected utility value (0.98 utile) compared with the other two strategies of visual inspection (0.84 utile) or referring to one of four diagnostic tests (0.74-0.82 utile). CONCLUSION: Although the general conclusion of this analysis agrees with current recommendations, this analysis offers a more complete mathematical model that provides a unified value for each strategy (i.e. expected utility value) thus allowing for complex quantitative comparison between strategies. This paper provides a specific example of how decision-tree analysis can be a powerful tool in guiding dental practice.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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; a candidate call from one teacher head, not a consensus.
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".