A population‐based assessment of melanoma: Does treatment in a regional cancer center make a difference?
Bibliographic record
Abstract
BACKGROUND: Regionalization of care to specialized centers has improved outcomes for several cancer types. We sought to determine if treatment in a regional cancer center (RCC) impacts guideline adherence and outcomes for patients with melanoma. METHODS: In Alberta, Canada, 561 patients with stage I-IIIC primary melanoma were diagnosed between January 2009 and December 2010. The electronic health record was used to capture demographic and pathologic data. Provincial guidelines for sentinel lymph node biopsy (SLNB) and wide local excision (WLE) are based on recommendations of several pre-existing guidelines including the National Comprehensive Cancer Network. RESULTS: 148 of 561 patients were identified as having been treated at a RCC. Median follow-up was 45 months. Patients treated at the RCC presented with higher stage melanomas. The RCC was more likely to follow guideline recommendations for performing SLNB (81.3% vs. 55.4%, P < 0.0001) but not for the extent of WLE (76.6% vs. 84.1%, P = 0.054). Overall survival was impacted by tumor thickness (HR 1.14, P < 0.0001), ulceration (HR 5.58, P < 0.0001), and mitoses (HR 0.59, P = 0.05). CONCLUSIONS: The RCC more closely followed guidelines for SLNB but not for WLE. Despite patients treated at the RCC presenting with a more advanced stage, overall survival and disease-free survival appear to not be affected by treatment center.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".