Evaluation of clinical instructors performance in radiographic assessment of primary molar furcation areas: A pilot study
Bibliographic record
Abstract
This study aimed to evaluate the diagnostic performance, agreement, and consistency of pediatric dentistry instructors in radiographic diagnosis of furcation lesions. Radiographic identification of furcation lesions in primary molars is challenging due to the superimposition of anatomic features and the developing permanent tooth buds. Clinical instructors’ inconsistencies in diagnosing this lesion may compromise patient outcomes and student training. Eight clinical instructors assessed 20 radiographs in two blinded rounds. A validated reference diagnosis dataset was established by two calibrated specialists, using paired pre-treatment and follow-up radiographs. Total and subgroup analysis was performed between instructors with and without specialty training. Diagnostic performance was measured by sensitivity, specificity, and accuracy. Agreement and consistency were evaluated using Fleiss’ and Cohen’s Kappa. Overall sensitivity, specificity, and accuracy were 0.75, 0.77, and 0.76, respectively, with no significant differences between pediatric specialists and general dentists (p=0.79). Agreement was moderate overall (K=0.50 - 0.59), ranging from fair to moderate in general dentists and substantial in specialists. Intra-rater consistency was substantial to almost perfect overall and in both groups (K=0.62- 0.82). Instructors demonstrated satisfactory accuracy and high intra-rater consistency in diagnosing furcation lesions, however inter-rater agreement was suboptimal. Standardized guidelines or could improve calibration and teaching reliability.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".