Decision making on detection and triage of oral mucosa lesions in community dental practices: screening decisions and referral
Bibliographic record
Abstract
UNLABELLED: Oral cancer is a substantial, often unrecognized issue globally, with close to 300 000 new cases reported annually. It is a management conundrum: a cancer site that is easily examined; yet more than 40% of oral cancers are diagnosed at a late stage when prognosis is poor and treatment can be devastating. Opportunistic screening within the dental office could lead to earlier diagnosis and intervention with improved survival. OBJECTIVE: To describe how clinicians make decisions about referral based on the risk classification of the lesion. METHODS: Eighteen dentists from 15 dental offices participated in a 1-day workshop on oral cancer screening. Participants then screened patients (medical history, conventional oral exam, fluorescent visualization examination) in-office for 11 months, triaging patients by apparent clinical risk: low risk (common benign conditions, geographic tongue, candidiasis, trauma), intermediate risk (lichenoid lesions) and high risk (white or red lesions or ulcers without apparent cause). Clinicians made the decision on which lesions to reassess in 3 weeks based on risk assessment and clinical judgment. Lesions of concern were seen by a community facilitator or referred to an oral medicine specialist. RESULTS: Of 2542 patients were screened, and 389 lesions were identified (15% of patients). 350 were determined to be low risk (90%), 19 intermediate risk (IR) (5%), and 20 high risk (HR) (5%). One hundred and sixty-six (43%) patients were recalled for 3-week reassessment: 90% of HR lesions, 63% of IR lesions (63%), and 39% of low-risk lesions. Compliance to recall was high (92% of cases). Reassessment eliminated the referral of 99/166 (60%) of lesions that had resolved. six lesions were biopsied with three low-grade dysplasias identified. CONCLUSIONS: Three key decision points were tested: risk assessment, need for reassessment, and need for referral. A 3-week reassessment appointment was invaluable to prevent the unnecessary referral due to confounders. There is a need for a well-defined triage pathway to facilitate oral cancer screening and a methodical and consistent approach to opportunistic screening in the dental office.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".