Increased risk of hip fracture in the elderly associated with prochlorperazine: is a prescribing cascade contributing?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it