Novel Approach to Improving Specialist Access in Underserved Populations with Suspicious Oral Lesions
Bibliographic record
Abstract
Late detection and specialist referral result in poor oral cancer outcomes globally. High-risk LRMU populations usually do not have access to oral medicine specialists, a specialty of dentistry, whose expertise includes the identification, treatment, and management of oral cancers. To overcome this access barrier, there is an urgent need for novel, low-cost tele-health approaches to expand specialist access to low-resource, remote and underserved individuals. The goal of this study was to compare the diagnostic accuracy of remote versus in-person specialist visits using a novel, low-cost telehealth platform consisting of a smartphone-based, remote intraoral camera and custom software application. A total of 189 subjects with suspicious oral lesions requiring biopsy (per the standard of care) were recruited and consented. Each subject was examined, and risk factors were recorded twice: once by an on-site specialist, and again by an offsite specialist. A novel, low-cost, smartphone-based intraoral camera paired with a custom software application were utilized to perform synchronous remote video/still imaging and risk factor assessment by the off-site specialist. Biopsies were performed at a later date following specialist recommendations. The study's results indicated that on-site specialist diagnosis showed high sensitivity (94%) and moderate specificity (72%) when compared to histological diagnosis, which did not significantly differ from the accuracy of remote specialist telediagnosis (sensitivity: 95%; specificity: 84%). These preliminary findings suggest that remote specialist visits utilizing a novel, low-cost, smartphone-based telehealth tool may improve specialist access for low-resource, remote and underserved individuals with suspicious oral lesions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".