Increased risk of hip fracture in the elderly associated with prochlorperazine: is a prescribing cascade contributing?
Bibliographic record
Abstract
PURPOSE: To examine the prescribing of prochlorperazine secondary to the prescribing of a medicine which could lead to symptoms for which prochlorperazine is indicated and commonly used. Given the range of potential hypotensive, sedative, dystonic and other extra-pyramidal side effects associated with prochlorperazine, its association with hip fracture was also examined. METHODS: Prescription/event sequence symmetry analyses were undertaken from 1st January 2003 to 31st December 2006, using administrative claims data from the Department of Veterans' Affairs, Australia. This method assesses asymmetry in the distribution of an incident event (either prescription of another medicine or hospitalization) before and after the initiation of prochlorperazine. Crude and adjusted sequence ratios (ASR) with 95% confidence intervals (CI) were calculated. RESULTS: A total of 34 235 persons with incident use of prochlorperazine were identified during the study period. Statistically significant positive associations were found for a number of commonly used medicines, including cardiovascular medicines, NSAIDs, opioids and sedatives and the subsequent initiation of prochlorperazine that ranged from 1.07 (95%CI 1.01-1.14) for diuretics to 1.50 (95%CI 1.40-1.61) for statins. Prescription event analysis showed a 49% (95%CI 1.19-1.86) increased risk of hospitalisation for hip fracture following dispensing of prochlorperazine. CONCLUSIONS: Prescribers should consider the possible contributing role of newly initiated medicines with the potential to cause of dizziness, and where possible address this through dose reduction or cessation of the medicine, rather than prescribing prochlorperazine.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".