Cancer incidence in a trial of an antiapoptotic agent for Parkinson's disease
Bibliographic record
Abstract
We performed a placebo-controlled trial of CEP-1347, an inhibitor of neuronal apoptotic cell death, in patients with early Parkinson's disease (PD) to determine whether long-term therapy would slow disability progression. This also provided an opportunity to monitor cancer incidence in a large cohort of PD patients followed prospectively including periods before and after patients developed disability requiring dopaminergic therapy. This was a multicenter study of 806 patients with early PD, without disability requiring dopaminergic therapy, assigned randomly to placebo or one of three doses of CEP-1347. Patients were monitored for an average of 1.8 years (1,467 patient-years) with routine cancer screening evaluations and annual skin examinations by a dermatologist. There was no significant excess of cancers among patients taking CEP-1347 compared with placebo for any cancer type (all P > 0.1). Nonmelanoma skin cancers were the most common cancer type observed. The incidence of melanomas was 20.9 times that predicted in the general population. Most melanomas occurred in patients who had never taken dopaminergic therapy. We found no evidence that CEP-1347 affected cancer incidence within 2 years of follow-up. Melanoma occurrence in our PD patients was greater than predicted compared with the general population and was unrelated to dopaminergic therapy. Clinical surveillance of PD patients for melanoma may be warranted.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".