Evaluating the Efficacy of Skin Cancer Referrals – Insights from Northern Ontario
Bibliographic record
Abstract
Abstract Purpose/Background: Skin cancer is the most common type of cancer that can be cured if identified and treated early. The efficacy of the skin cancer referral process has not been elucidated, and it is unclear if the lack of primary care providers has had an effect on patients in Northern Ontario. Our study aimed to illustrate a typical skin cancer patient’s journey from prediagnosis to treatment and to characterize prevalence, clinical management, and patient outcomes. Materials and Methods: A retrospective review of patient electronic records referred to a plastic surgeon in Thunder Bay for suspicious skin lesions was conducted over a 7-year period. Referrals received by dermatologists were excluded to assess the ability of primary care providers to identify cancerous skin lesions. Referral origin and urgency, presumed diagnosis, type of lesion, surgical intervention, wait times, and clinical outcomes were analyzed. Descriptive statistics were used. Results: A total of 376 referrals were reviewed, and 250 were included. Of these, 214 were sent by family physicians, and 101 referrals were sent without a specified urgency. A total of 156 referrals were sent with a suspected diagnosis from the referring provider with a presumed diagnosis accuracy of 67.9%, as confirmed by final pathology. Basal cell carcinoma was the most common lesion. The mean wait time from referral to consultation and from consultation to surgical intervention was 22 and 35 days. Conclusions: Most referrals were sent from family physicians, and they were reliable in recognizing cancerous skin lesions. Wait times and patient outcomes were acceptable given the lack of access to plastic surgeons and low local recurrence rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".