Neuroleptic Malignant Syndrome—An 11-Year Longitudinal Case-Control Study
Bibliographic record
Abstract
OBJECTIVE: To describe patients with neuroleptic malignant syndrome (NMS), to establish occurrence of NMS, to investigate risk factors of NMS, and to investigate mortality associated with NMS. METHOD: We conducted a longitudinal register linkage case-control study of NMS. RESULT: In health care registers covering the period from 1996 to 2007, we identified, among 224 372 patients with organic, psychotic, affective, or neurotic diagnosis, 83 patients with NMS, equivalent to an occurrence of 0.04%. Treatment with second-generation antipsychotics (SGAs) in the 3 months preceding admission increased the NMS risk (OR 4.66; 95% CI 1.96 to 11.10) and also first-generation antipsychotics (FGAs) of high potency (OR 23.41; 95% CI 5.29 to 103.61) and mid potency (OR 4.81; 95% CI 1.96 to 11.79), and depot antipsychotics (OR 4.53; 95% CI 1.60 to 12.80). Benzodiazepines (BDZs) also increased the risk of NMS (OR 3.43; 95% CI 1.68 to 12.80). NMS was associated with an increased mortality (HR 1.88; 95% CI 1.19 to 2.98) in patients, compared with sex-, age-, and diagnosis-matched control subjects, but no significant difference in mortality between patients and control subjects was observed after the initial 30 days (P = 0.27). CONCLUSIONS: The occurrence of NMS is low, and the prediction of NMS is difficult. Previous treatment with FGAs, SGAs, and BDZs was identified as a risk factor for developing NMS. NMS increased mortality within 30 days after NMS.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".