Prevalence of Carotid Atheroma Plaque on Panoramic Imaging in Type II Diabetes Mellitus Patients: A Systematic Review
Bibliographic record
Abstract
Background: Hyperglycemia and other risk factors lead to the formation of plaques in the neck area of the carotid artery. Carotid atheroma plaque may predispose or increase risk of cerebrovascular accident. Panoramic radiographs can pick up mere calcified plaque and early referral to a physician would prevent long-term complications. Materials and Methods: The electronic and hand search of studies published until October 2022, yielded a total of 17 articles, out of which 4 studies met the inclusion criteria. All studies examined the prevalence of carotid atheroma in panoramic images of patients with type 2 diabetes. The risk of bias was assessed by the Newcastle-Ottawa scale. Results: This systematic review search yielded 168 publications from various databases. After reading the full text, four articles were selected and met the selection criteria. All four studies showed the level of evidence IV which was assessed using Agency for Health Research and Quality (AHRO) guidelines. Conclusion: The prevalence of carotid atherosclerotic plaques is higher in diabetic patients compared to non-diabetic patients. Literature-based evidence suggests that carotid atheroma can be detected on panoramic images and the patients can be referred to a doctor for further examination and necessary treatment.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| 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) | 0.004 | 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".