Predictors of Functional Decline in Hospitalized Elderly Patients: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: This article will systematically review the methodological characteristics and results of studies of variables and indices that predict functional decline in older hospitalized patients. METHODS: We restricted this review to original longitudinal studies of predictors of either physical functional decline or nursing home admission among patients aged 60 and older. Two reviewers independently abstracted information on methodological characteristics and substantive results. RESULTS: Thirty articles were identified, derived from 27 different studies, reporting on 33 substudies. Substantial variability was found with respect to study design, outcomes measured, period of follow-up, predictors investigated, and analytic methods. Multivariable predictive indices were significantly associated with adverse outcomes in the majority of studies that investigated them, as were the following variables: age, diagnosis, activities of daily living, cognitive impairment (including delirium), and residence. CONCLUSIONS: The methodological heterogeneity of the studies identified limits quantitative synthesis of the results. Predictive indices for hospitalized elders appear to have moderate short-term predictive ability.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".