Risk factors for skin cancer and solid tumors in newly diagnosed patients with chronic lymphocytic leukemia and the impact of skin surveillance on survival
Bibliographic record
Abstract
A retrospective analysis on 587 patients with chronic lymphocytic leukemia (CLL) assessed risk factors for skin cancer and the influence of skin cancers on survival and incidence of solid tumors (STs). Patients underwent skin surveillance and were followed for a median of 6.65 years. The relative risk for skin cancer increased prior to CLL diagnosis rising 4-fold one-year post-diagnosis. Independent predictors for skin cancer were male gender (p = .0001), age ≥70 years (p = .0036) and prior chemotherapy (p = .0116). There was no increase in mortality from skin cancer and neither skin cancer nor chemotherapy increased the risk for a ST. The development of a ST was an independent predictor of survival (p < .0001) and 43% of deaths were related to STs. Thus, regular skin surveillance can prevent increased mortality from skin cancer, but not STs, in CLL. Close skin monitoring is required for elderly males who received chemotherapy.
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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 | 0.002 |
| 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) | 0.001 | 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".