Does increased medication use among seniors increase risk of hospitalization and emergency department visits
Bibliographic record
Abstract
Objective: to examine the extent of the health risks of consuming multiple medications among the older population. Data sources/study setting: Secondary data from the period 2004-2006. The study setting was the province of Ontario, Canada, and the sample consisted of individuals aged 65 years or older who responded to a national health survey. Study design: We estimated a system of equations for inpatient and emergency department (ED) services to test the marginal effect of medication use on hospital services. We controlled for endogeneity in medication use with a two-stage residual inclusion approach appropriate for non-linear models. Principal findings: Increased prescription drug use has the effect of increasing the likelihood of both being admitted into hospital and visiting a hospital ED. Each additional medication is associated with a 2% increase the likelihood of hospitalization and ED visit, after controlling for past utilization, health status, the endogeneity of medication use, and the unobserved factors that may affect the use of both services. Conclusions: Multiple medications appear to increase the risk of hospitalization among seniors covered by a universal prescription drug plan. These results raise questions about the appropriateness of medication use and the need for increased oversight of current prescribing practices.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".