Prognostic factors in determining the outcome of head and neck cutaneous melanoma
Bibliographic record
Abstract
Melanoma arises from melanocytes in the skin (cutaneous) or mucosa (mucosal). It is one of the most aggressive skin cancers when compared to other, non-melanoma skin cancers. Worldwide, melanoma represents 4% of all skin cancers, but is responsible for 80% of skin cancer deaths, and 1-2% of all cancer deaths overall. In Canada, there are approximately 6500 new cases of malignant melanoma diagnosed per year. Our objective is to evaluate the impact of individual prognostic factors on the outcome of malignant melanoma in the head and neck region. The study will involve a review of about 1600 paper and electronic medical records of a historical cohort of patients diagnosed in the province of Manitoba from 1970 to 2012 whose diagnoses can be separated into invasive and in situ. The information collected will include demographic risk factors, factors associated with malignant transformation of in situ disease, extent of disease at presentation, treatment, pathology, patterns of failure, salvage, and final outcome in a pre-designed data collection sheet. Survival will be estimated by Kaplan Meier method and the impact of non-cancer deaths will be assessed by competing risk analysis. The effect of various prognostic factors such as the stage of tumour, margin status, Breslow's index, pathological type, treatment modality, and co-morbidity on disease free survival and cause specific survival will be analyzed by Cox Proportional Hazard model for independent variables using SPSS 22.0.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".