Neuroleptic malignant syndrome in Mexico.
Bibliographic record
Abstract
BACKGROUND: Neuroleptic Malignant Syndrome (NMS) is an uncommon but potentially fatal complication of antipsychotic and neuroleptic drug treatment. OBJECTIVES: This study estimated the frequency, clinical presentation, and outcome of NMS in a referral center for neurological, neurosurgical and psychiatric disorders in Mexico. METHODS: The authors conducted a thorough search of psychiatry, neurology, neurosurgery and intensive care unit records for cases of NMS during the 10-year period between 1990 and 1999. They examined the clinical features, course and treatment of the NMS episodes, and performed a follow-up survey for residual symptoms and clinical outcome. The mean time to follow-up assessment was 36 months. RESULTS: A total of eight of 4831 neuroleptic-treated patients had an episode of NMS (incidence 0.165%). Seven of the eight patients were treated with haloperidol. Other neuroleptics agents associated with NMS were depot pipotiazine palmitate and levomepromazine maleate. One patient received lithium concomitantly. No fatal outcome was found. Only one patient developed persistent clinical sequelae, consisting of extrapyramidal and cerebellar symptoms, three years after the NMS episode. CONCLUSIONS: The slightly low frequency of NMS found in this study compared with studies conducted in other countries may be attributable to the advent and use of newer atypical antipsychotics in Mexico, the rigorous demands for NMS diagnostic criteria and the lack of familiarity with the diagnosis between physicians.
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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) | 0.001 | 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".