Long‐term skill improvement among general dental practitioners after a short training programme in diagnosing calcified carotid artery atheromas on panoramic radiographs
Bibliographic record
Abstract
PURPOSE: To study general dental practitioners (GDPs) ability to detect calcified carotid artery atheromas (CCAAs) in panoramic radiographs (PRs) and if their diagnostic accuracy in long term is improved after a short training programme. METHODS: Fourteen GDPs had their diagnostic accuracy regarding CCAA in PR assessed at baseline, 2 weeks and 1 year after training. Comparison was made with a reference standard based on consensus results from two experienced oral and maxillofacial radiologists. At each session, 100 radiographs were assessed individually by the GDPs. After the baseline assessment, the GDPs participated in a 2-hour training programme comprising a lecture and diagnostic training by calibration. The GDPs results before and after training were compared, as well as between follow-up sessions. RESULTS: A significant improvement in diagnostic accuracy was observed with increased sensitivity (from 41.8% to 55.7%, P = 0.02) without a significant decrease in specificity (from 87.2% to 86.7%, P = 0.87). The Kappa values also increased (from 0.66 to 0.71, P = 0.04). At 1-year follow-up, the improvement compared to baseline remained significant. There were no significant changes between the 2-week and 1-year follow-up assessment. CONCLUSION: A short training programme can significantly and sustainable improve GDPs diagnostic accuracy regarding CCAA.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".