Etiologic factors associated with p53 immunostaining in cutaneousmalignant melanoma
Bibliographic record
Abstract
Findings from a case-control study of cutaneous malignant melanoma (CMM) in Queensland, Australia, suggest that melanomas exhibiting p53 immunostaining possess different risk factors from those of other melanomas. To further explore this hypothesis, a case-only analysis of risk factors for p53 immunostaining with anti-p53 MAb DO-7 was undertaken in 523 people diagnosed with CMM in Canada and Australia. Phenotypic factors and past sun exposure were measured using a self-administered questionnaire and telephone interview. The presence of strong p53 staining (>10% of cell nuclei positively stained vs. <1% staining) was positively associated with some indicators of high cumulative sun exposure: lentigo maligna melanoma subtype (OR = 3.2 vs. superficial spreading subtype), melanoma location on the head and neck (OR = 2.8 vs. back), histopathologic evidence of solar elastosis (OR = 2.1) and previous diagnosis of nonmelanoma skin cancer (OR = 2.4). Strong staining was negatively associated with high nevus density on the back (OR = 0.2 for >25 nevi vs. 0-3 nevi) and histologic evidence of a coexisting nevus (OR = 0.3). Other factors associated with strong p53 immunostaining include greater Breslow thickness (OR = 7.4 for >4.00 vs. <0.76 mm), male sex (OR = 2.2) and dense freckling (OR = 6.6 vs. few freckles). Of these, thickness, male sex, dense freckling, low nevus density on the back, histologic subtype and history of nonmelanoma skin cancer appeared to be independently associated with strong p53 staining. Our findings are consistent with the Queensland study in suggesting that variables indicating high accumulated sun exposure are positively associated with p53 staining and that an increased number of nevi is positively associated with its absence; they may reflect etiologic and pathogenetic heterogeneity in melanoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".