Cumulative assessment of factors leading to restorative decisions in an educational environment. A graphical demonstration using an in vitro case.
Bibliographic record
Abstract
Even though accuracy and inter-examiner variation assessments of borderline restorative items have been previously reported, no attempt has been made to replicate the effect of cumulative, sequential diagnostic and treatment planning decisions. This study assesses the cumulative effect of factors indicating restorative needs by evaluating how readily tooth restoration was proposed on the basis of restoration quality and presence of caries (compared to gold standards). Ninety-one senior dental students in Mexico City (79% female; mean age 22.8 years) assembled in 19 teams of five students each. They sequentially evaluated 56 restored and unrestored posterior teeth in an in vitro model. Each student examined the set, removed those teeth needing restorative intervention and returned the remaining set for examination by a second student. When the second assessment was completed, the remaining teeth were turned over to the third teammate and so on. Teeth were subsequently assessed for restoration quality and enamel and dentinal caries. When a tooth showed a carious lesion, a dentinal lesion or a defective restoration, the likelihood of it being selected for restorative treatment increased. When more than one feature was present, the chances of the tooth being selected more frequently and earlier increased, accordingly. The specificity of restorative treatment needs was not excellent. A strong graphical association between the presence of caries and/or defects in restorations with proposed restorative treatment was demonstrated using a non-quantitative research model. The more abundant these features were, the higher the probability appeared for a tooth to fit the clinical picture suitable for restorative intervention.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".