Assessing and Monitoring Antipsychotic-Induced Movement Disorders in Hospitalized Patients: A Cautionary Study
Bibliographic record
Abstract
OBJECTIVE: To assess the amount of documentation and level of assessment provided by attending physicians and nursing staff in regard to extrapyramidal symptoms (EPS) experienced by hospitalized patients with varied DSM-IV diagnoses regularly treated with antipsychotic medication. METHOD: We examined the medical records of 204 hospitalized patients. All medical records were examined retrospectively from consecutive admissions beginning in January 1996. We identified demographics, length of hospitalization, diagnosis, and antipsychotic and adjunct medication. EPS were classified into dystonia, parkinsonism, akathisia, and tardive dyskinesia (TD). For each type of EPS, 2 independent raters rated the quality of assessment based on dimensions of severity, location, and laterality. RESULTS: The extent of interrater agreement was found to be 91.1%. Parkinsonism and akathisia were more frequently assessed, compared with TD and dystonia. However, the medical records examined showed generally poor assessment and documentation of EPS. The percentage of medical records with "no description" for each EPS classification was as follows: dystonia (89%), parkinsonism (71%), akathisia (67%), and TD (94%). CONCLUSIONS: The major finding of this study was a high rate of failure to document the assessment and course of EPS. This finding suggests that clinicians do not recognize the importance of documenting these significant adverse events. This shortcoming can be corrected with increased awareness of EPS and increased training in their physical examination.
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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 | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".