Prescription drug claims following a traumatic spinal cord injury for older adults: a retrospective population-based study in Ontario, Canada
Bibliographic record
Abstract
STUDY DESIGN: Retrospective cohort study. OBJECTIVES: The objectives for this study were to examine the prevalence of polypharmacy for people with traumatic spinal cord injury (SCI) following injury and to determine risk factors. SETTING: Ontario, Canada METHODS: We used provincial-level administrative health services data of publicly funded healthcare encounters housed at the Institute for Clinical Evaluative Sciences, Toronto, Ontario. We examined prescription medications dispensed over a 1 year period post injury for persons 66+ years with an index traumatic SCI between 2004 and 2014. Polypharmacy was defined as being on 10 or more drug classes. Descriptive and analytical statistics were conducted. Relative risks and 95% confidence limits for factors related to polypharmacy were calculated using a robust Poisson multivariate regression model. RESULTS: We identified 418 cases of persons with traumatic SCI during the observation window. A total of 233 patients (56%) were taking at least 10 drug classes in the year following discharge from care for traumatic SCI. The mean number of drug classes taken post injury was 11 (SD = 6). Continuity of care was significantly associated with polypharmacy, with a higher continuity of care (having at least 75% of visits with the same doctor) reducing the risk of polypharmacy. The most common drugs prescribed were laxatives, opioids and cardiovascular-related drugs. CONCLUSION: Findings suggest that polypharmacy is extensive among older adults with traumatic SCI. Persons with better continuity of care are less likely to have polypharmacy compared to those with less continuity. SPONSORSHIP: This project was funded by a Connaught New Investigator Award (University of Toronto), and the Craig H. Neilsen Foundation Psychosocial Research Pilot Grant (Grant #441259).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".