Keratinocyte Carcinoma: Canadian Landscape and an Evidence-based Approach to Follow-up
Bibliographic record
Abstract
Dermatologists play a vital role in the early detection, prevention and effective management of skin cancer in patients with a prior history of the disease. Regular monitoring and timely interventions greatly enhance the overall prognosis and quality of life for patients with skin cancer. Dermatologists possess the requisite expertise to accurately diagnose and oversee the management of cutaneous skin cancers. Skin cancer screening via total body skin exam (TBSE) is often considered one of the safest, easiest, and most cost-effective tests in medicine. Despite dermatologists’ ability to offer such invaluable care for this patient population, offering routine skin checks for all patients with a prior history of skin cancer becomes exceptionally challenging given the high demand for dermatology care across Canada. It is important for dermatologists to maximize the efficiency of care during TBSEs by adhering to evidence-based guidelines when determining the frequency and duration of follow-up. These guidelines also provide a solid foundation for discussions with patients regarding the rationale for discharge back to their primary care provider.
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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 | 0.041 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".