Incidence of Parkinson's disease among hospital patients with methamphetamine‐use disorders
Bibliographic record
Abstract
Because methamphetamine exposure to experimental animals can damage brain dopamine neurones, we examined whether hospital patients diagnosed with methamphetamine-related disorders might have greater risk of subsequent admission with a Parkinson's disease diagnosis. This was a population-based cohort study using all statewide inpatient hospital discharge records from July 1, 1990, through June 30, 2000, in California, USA, in which subjects aged at least 50 years were followed for up to 10 years. Individuals with reported methamphetamine-related conditions (n = 1,863; ICD-9 codes 304.4, 305.7, 969.7, and E854.2) were matched on demographic variables and follow-up time with those with primary appendicitis conditions (n = 9,315). The appendicitis group had a Parkinson's disease incidence rate no different than the rate found among members of a large health maintenance organization in California. Cox regression procedures were used to estimate group differences in the rates of receiving a subsequent inpatient diagnosis of Parkinson's disease (ICD-9 332.0). The methamphetamine group showed increased risk of a subsequent admission with Parkinson's disease compared with that of the matched appendicitis group (adjusted hazard ratio = 2.65, 95% CI, 1.17-5.98, P= 0.019). Study limitations include a population limited to hospital admissions, an uncertainty regarding diagnostic validity of the ICD-9 code 332.0 (Parkinson's disease), and a small number of incident cases with suspected Parkinson's disease. We strongly emphasize the preliminary nature of the findings. Nevertheless, these data, requiring replication, provide some evidence that methamphetamine users might be at greater than normal risk for developing Parkinson's disease.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".