The Current State of Clinical Diagnostic Algorithms for Mucosal Oral Lesions: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Diagnosing oral lesions remains challenging for many dentists. Despite the availability of diagnostic algorithms, there is a dearth of comprehensive evidence synthesis and a discussion on their clinical and pedagogical applicability. METHODS: A scoping review was conducted to identify: (1) algorithms or flow diagrams that help clinicians to diagnose oral lesions in a clinical setting without additional software; (2) publications in English; (3) all age groups; (4) algorithms for oral lesions of soft tissue only. We excluded those that are: (1) black-box; (2) required additional tests; (3) older versions; (4) for non-mucosal lesions, and (5) intended for self-screening. A keyword and MeSH term search was performed across three peer-reviewed publication databases and gray literature. RESULTS: Seventeen algorithms from 15 peer-reviewed manuscripts and 1 online course were identified. Most studies did not mention how the algorithms were developed, and none had been validated in a clinical setting. The algorithms often focused on one or two types of lesions and were incomplete in differential diagnoses. CONCLUSION: Few clinical diagnostic algorithms for oral lesions are available in the literature. Notably, there are no validated and comprehensive clinical diagnostic algorithms for oral mucosal lesions.
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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 | 0.031 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".