Depicting Errors in Clinical Decisions for Posterior Proximal Enamel Caries Lesions in Permanent Teeth Using the Fact Box Format
Bibliographic record
Abstract
OBJECTIVES: To depict restorative treatment recommendations of US dentists for posterior proximal enamel caries lesions detected with bitewing radiographs in permanent teeth. METHODS: The Fact Box format was utilized to depict the probabilities of restorative treatment recommendations made by US dentists for posterior proximal enamel caries lesions detected with bitewing radiographs in permanent teeth. Four case scenarios were considered, including patients at low caries risk versus those at high caries risk for two proportions (10% versus 38%) of proximal enamel caries lesions with external surface cavitation. RESULTS: The Fact Box showed that the decision to restore posterior proximal enamel caries lesion was more likely to be an incorrect decision (61-91%) in the four case scenarios considered. Meanwhile, the decision to not provide restorative treatment for posterior proximal enamel caries lesion was less likely to be erroneous (9-37%) in the four case scenarios considered. CONCLUSION: Using the Fact Box to depict restorative decision-making for posterior proximal enamel caries lesions in permanent teeth may improve communication of decisional probabilities and reduce restorative overtreatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".