Cost Comparisons of Managing Complex Facial Basal Cell Carcinoma: Canadian Study
Bibliographic record
Abstract
BACKGROUND: Basal cell carcinoma (BCC) is the most common human malignancy and accounts for over 60,000 new cases of cancer in Canada annually. Although expensive to the health care system, no Canadian studies have reported the costs involved in management. This study calculated the costs of managing high-risk BCCs using radiotherapy (RT) and Mohs micrographic surgery (MMS). METHODS: Forty-nine consecutive complex BCC cases presenting to a skin cancer referral center were collected prospectively. All were located on the head and neck and were either recurrent disease or located in "at risk" sites such as the eye, ear, lip, or nose. All patients underwent MMS. A radiation oncologist reviewed each case retrospectively. The costs of MMS were the actual costs of the procedure, with an additional amount added to account for the technical costs of the surgery. The costs of RT included physician fees and technical fees. A sensitivity analysis was performed using known recurrence rates from the medical literature. RESULTS: Five patients were excluded from the comparative analysis because radiation was not recommended (age < 50 years or radiation would overlap with a previous radiation field). The direct cost of treating a patient with a single BCC was $871 (range $630-1,159) using MMS and $3,625 (range $3,430-3,971) using RT. The costs were significantly higher for patients with multiple tumors with both modalities (p = .02 for both). The direct costs of a "5-year cure" were $952 (range $644-1,647) for MMS and $3,758 (range $3,564-4,675) for RT. CONCLUSIONS: This study attempted to document the costs associated with two well-recognized and effective methods of treating complex BCC in Ontario. Subgroup analysis revealed independent associations between aggressive histology, larger size, and complexity of surgical closure with higher costs. Although we did notice a trend toward greater costs in patients with recurrent disease, in males, younger patients, and tumors present for > 1 year, these did not reach significance within our sample size. Despite the limitation that treatment costs may be center and provincially dependent, we hope this preliminary report will initiate further study into comparing Canadian costs of managing skin cancer.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".