Bibliographic record
Abstract
OBJECTIVE: To evaluate the possible association of Parkinson disease (PD) and melanoma in North America. DESIGN, SETTING, AND PATIENTS: Thirty-one centers enrolled patients with idiopathic PD. At visit 1, a neurologist obtained a medical history. At visit 2, a dermatologist recorded melanoma risk factors, performed a whole-body examination, and performed a biopsy of lesions suggestive of melanoma for evaluation by a central dermatopathology laboratory. We compared overall prevalence of melanoma with prevalence calculated from the US Surveillance Epidemiology and End Results (SEER) cancer database and the American Academy of Dermatology skin cancer screening programs. RESULTS: A total of 2106 patients (mean [SD] age, 68.6 [10.6] years; duration of PD, 7.1 [5.7] years) completed the study. Most (84.8%) had received levodopa. Dermatology examinations revealed 346 pigmented lesions; dermatopathological findings confirmed 20 in situ melanomas (0.9%) and 4 invasive melanomas (0.2%). In addition, histories revealed 68 prior melanomas (3.2%). Prevalence (5-year limited duration) of invasive malignant melanoma in the US cohort of patients with PD (n = 1692) was 2.24-fold higher (95% confidence interval, 1.21-4.17) than expected in age- and sex-matched populations in the US SEER database. Age- or sex-adjusted relative risk of any melanoma for US patients was more than 7 times that expected from confirmed cases in American Academy of Dermatology skin cancer screening programs. CONCLUSIONS: Melanoma prevalence appears to be higher in patients with PD than in the general population. Despite difficulties in comparing other databases with this study population, the study supports increased melanoma screening in patients with PD.
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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 | 0.001 | 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) | 0.002 | 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".