Integration of new technology into a high-risk underserved community: Pilot studies within an oral cancer screening clinic.
Bibliographic record
Abstract
B9 Oral cancer is a deadly disease characterized by both high mortality and morbidity. Early identification of this disease in the community is key to its management; however, globally, we have yet to develop effective screening regimes in such settings. This is especially true for underprivileged populations with marginalized life styles and poor access to care, but where the need is greatest. Objective : To pilot an oral cancer screening intervention in a clinic that services a high-risk hard-to-reach community in the Vancouver Downtown Eastside (DTES), utilizing a triage system under development in British Columbia. This system will integrate visualization, computer imaging and molecular tools to identify cases at risk in the community and triage them to treatment. This abstract describes the initial phase of this study. Method: In September 2004, an oral cancer-screening clinic was established in a pre-existing community dental clinic in the DTES. Patients attending the clinic for regular dental workups were offered screening, utilizing both conventional techniques and visualization aids (fluorescence visualization and toluidine blue retention). Samples of exfoliated cells were collected from lesion and control sites for future assessment of phenotypic change with high throughput computer technologies. Results: To date, 200 of 204 (98%) patients approached have agreed to screening. Of these, the majority were at high-risk for oral cancer: ever smokers (89%) and regular consumers of alcohol (89%), often immunocompromised (HIV and HCV), with a high usage of illicit drugs. Trauma, infection and inflammation were common - often masking the visualization of clinical features. Leukoplakia was seen in 31 patients and all showed significant alteration in fluorescence, with 13 (42%) also showing toluidine blue staining. To date, 12 of these 13 cases have been biopsied, showing 2 cancers and 8 precancers. Conclusion: This study demonstrates the feasibility of establishing screening activities in dental clinics in poor, medically underserved populations and supports the utility of screening devices in such groups. Future work will integrate computer technologies to facilitate the differentiation of lesions at risk (and requiring biopsy) among cases in which the disease could be masked by chronic trauma and infection. (Supported by grants R01DE13124, R01DE17013, NIDCR, and salary support to CFP from CIHR).
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".