Factors related to the presentation of thin and thick nodular melanoma from a population‐based cancer registry in Queensland Australia
Bibliographic record
Abstract
BACKGROUND: Worldwide, the incidence of thick melanoma has not declined, and the nodular melanoma (NM) subtype accounts for nearly 40% of newly diagnosed thick melanoma. To assess differences between patients with thin (<or=2.00 mm) and thick (>or=2.01 mm) nodular melanoma, the authors evaluated factors such as demographics, melanoma detection patterns, tumor visibility, and physician screening for NM alone and compared clinical presentation and anatomic location of NM with superficial spreading melanoma (SSM). METHODS: The authors used data from a large population-based study of Queensland (Australia) residents diagnosed with melanoma. Queensland residents aged 20 to 75 years with histologically confirmed first primary invasive cutaneous melanoma were eligible for the study, and all questionnaires were conducted by telephone (response rate, 77.9%). RESULTS: During this 4-year period, 369 patients with nodular melanoma were interviewed, of whom 56.7% were diagnosed with tumors <or=2.00 mm. Men, older individuals, and those who had not been screened by a physician in the past 3 years were more likely to have nodular tumors of greater thickness. Thickest nodular melanoma (4 mm+) was also most common in persons who had not been screened by a physician within the past 3 years (odds ratio, 3.75; 95% confidence interval, 1.47-9.59). Forty-six percent of patients with thin nodular melanoma (<or=2.00 mm) reported a change in color, compared with 64% of patients with thin SSM and 26% of patients with thick nodular melanoma (>2.00 mm). CONCLUSIONS: Awareness of factors related to earlier detection of potentially fatal nodular melanomas, including the benefits of a physician examination, should be useful in enhancing public and professional education strategies. Particular awareness of clinical warning signs associated with thin nodular melanoma should allow for more prompt diagnosis and treatment of this subtype.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".