Are Panoramic Images a Good Tool to Detect Calcified Carotid Atheroma? A Systematic Review
Bibliographic record
Abstract
To investigate the reliability of panoramic dental images to detect calcified carotid atheroma, electronic databases (PubMed, IEEE/Xplore and Embase) were searched. Outcomes included cerebrovascular disease events, cardiovascular disease events, patient previous diseases, and combined endpoints. Risk of bias was evaluated using the Newcastle-Ottawa Scale. Hence, 15 studies were selected from 507 potential manuscripts. Five studies had a low risk of bias, while the remaining nine studies were found to have a moderate risk. Heterogeneous results were obtained but showed that patients with risk factors, such as obesity, diabetes mellitus, hypertension, and smoking, and with calcified carotid atheroma on panoramic images, have a higher prevalence than healthy patients. The evidence in the literature was found to be equivocal. However, the findings of this systematic review exhibit that panoramic radiographs can be used for dental diagnosis and treatment planning, as well as to detect calcified carotid artery atheroma.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".