Epidemiological features and prognostic factors of cutaneous head and neck melanoma: a population-based study.
Bibliographic record
Abstract
OBJECTIVES: To describe the epidemiological features of cutaneous head and neck melanoma (CHNM) and to identify factors associated with mortality from this disease. DESIGN: A population-based cohort study. SETTING: Patients treated for CHNM in Ontario between January 1, 1994, and December 31, 2002, were identified through the provincial Cancer Registry. A Cox proportional hazards regression model was used to analyze the data. PATIENTS: A total of 2218 patients with CHNM were identified, comprising 15.8% of all melanomas in Ontario. The mean age of the cohort was 66 years (SD, 16 years); 1363 patients (61.5%) were males. MAIN OUTCOME MEASURE: Patients' vital status (dead or alive). RESULTS: The incidence of CHNM increased from 2.0 per 100,000 in 1996 to 2.7 per 100,000 in 2001, while mortality remained stable. The Cox proportional hazards regression model showed that increased age (hazard ratio [HR], 1.06; 95% confidence interval [CI], 1.04-1.06) and male sex (HR, 1.31; 95% CI, 1.03-1.66) had a significantly higher risk of death. Patients with lesions of the scalp and neck had a 53% higher risk of death than those with lesions of the face. Nodular melanoma (HR, 1.61; 95% CI, 1.17-2.24) had the worst prognosis compared with other morphological types. Increased tumor thickness (HR, 1.05; 95% CI, 1.03-1.07), ulceration (HR, 1.53; 95% CI, 1.08-2.07), and Clark level V (HR, 1.52; 95% CI, 1.01-2.22) were significantly associated with increased mortality. CONCLUSIONS: Our study demonstrated an increase in the incidence of CHNM. Advanced age, male sex, nodular morphological features, tumor thickness, ulceration, and Clark level V carried a significant risk of death, whereas facial melanomas had a favorable prognosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".