Neuroleptic‐induced <scp>P</scp>arkinsonism: <scp>C</scp>linicopathological study
Bibliographic record
Abstract
BACKGROUND: Drug-induced parkinsonism is a well-known complication of several different drugs--the most common being neuroleptic-induced parkinsonism. However, very few autopsies have been reported in such cases. METHODS: Patients assessed at Movement Disorders Clinic Saskatchewan are offered brain autopsy. Detailed clinical records are kept. RESULTS: Brains were obtained from 7 drug-induced parkinsonism patients with parkinsonian symptom onset coinciding with use of drugs known to produce parkinsonism. Six were on antipsychotics and 1 was on metoclopramide. Three cases were treated with levodopa for parkinsonism. In two cases, parkinsonian features reversed after stopping the offending agent. Both had autopsy evidence of preclinical PD. In 4 of the remaining 5, dopamine-blocking drugs were continued until death. In 4 of those 5, brain histology revealed no cause for the parkinsonism, but 1 had mild SN neuronal loss without Lewy bodies. CONCLUSION: This study shows that reversal of parkinsonism after discontinuing offending drugs does not indicate absence of underlying pathology. Neuroleptics can unmask preclinical PD in patients with insufficient SN damage for the disease to manifest clinically. Though the mechanism of sustained parkinsonian features after discontinuing neuroleptics remains to be established, it is unlikely that dopamine receptor block leads to retrograde SN neuronal degeneration. Furthermore, L-dopa does not appear to be toxic to SN.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".