FRAILTY AND USE OF HEALTH SERVICES IN INJURED SENIORS: A POPULATION-BASED STUDY
Bibliographic record
Abstract
Currently, most information on frailty in seniors comes from cohort or trials studies. Methodologies to identify frail seniors within secondary care data, both at patient and population level, are current surveillance priorities. Objectives: to measure frailty using health administrative databases and examine the association between frailty and medical services use among non-institutionalized seniors with a minor fracture. Methods: Population-based cohort built from the Quebec Integrated Chronic Disease Surveillance System, including seniors ≥ 65 years, non-institutionalized in the pre-fracture year. Frailty was measured using the ERA index. Multivariate Poisson analysis were used to examine the association between frailty level and use of emergency department (ED) and general practitioner (GP) services 1 year post-fracture, adjusting for confounders. Results: The cohort included 179,734 individuals (mean age 76.3 years, 74 % women). There were 13 % and 4.7%, frail and non-frail seniors, respectively. Our Poisson regression analyses show that, in the post-fracture year, ED and GP visits were significantly higher in frail VS non-frail seniors: adjusted relative risk (RR)= 2.95: 95% CI: 2.83–3.08 for ED visits and RR=1.24: 95% CI: 1.21–1.28 for GP visits. Conclusion: This study suggests that it is possible to characterize seniors’ frailty at a population level using health administrative databases. Furthermore, this study shows that non-institutionalized frail seniors require more health services after an incident minor fracture. Screening for frailty in seniors should be part of clinical management in order to identify those at high risk of needing more health services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".