COMORBIDITY, DISABILITY, AND FRAILTY PROFILES AS DETERZMINANTS OF HOSPITALIZATION AMONG OLDER ADULTS
Bibliographic record
Abstract
Population aging increases the need to better understand the determinants of hospitalization in order to improve the quality of services. However, usual health measures often prove to be poorly predictive of hospitalization. The aim of this research is to determine whether physiological health measures designed for older adults, namely the Short Physical Performance Battery (SPPB) (Guralnik et al., 1994), frailty phenotypes (Fried et al., 2001), and profiles of functional decline (Lunney et al., 2003), are better predictors of hospital use. We use longitudinal data from the International Mobility in Aging Study (IMIAS), carried out between 2012 and 2014 in four countries (Canada, Brazil, Colombia, Albania) among individuals aged 65–74 at baseline (n=1724). Differences between health systems provide additional insights into the determinants of hospitalization. Health profiles from 2012, besides other confounders, are used to explain hospitalization in 2014. SPPB scores are computed using objective measures of gait speed, chair-stands, and balance. Frailty phenotype variables are created according to Fried’s classification into robust, pre-frail, and frail. Four profiles of functional decline are created: (1) terminal illness – cancer and at least one ADL disability; (2) organ failure – heart or lung disease and at least 2 ADL disabilities, (3) low reserve – stroke or more than 2 ADL disabilities, and (4) other respondents. Sample attrition is corrected by means of a Heckman selection Probit model. Our results indicate that a score below 8 on SPPB, the pre-frailty phenotype, and a profile of organ failure are significantly associated with hospitalization. Cross-country differences are discussed.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".