The Predictive Utility of Functional Status at Discharge: A Population-level Cohort Analysis
Bibliographic record
Abstract
Abstract Background: Functional status is a patient-important, patient-centered measurement. The utility of functional status measures to inform post-discharge patient needs is unknown. We sought to examine the utility of routinely collected functional status measures gathered from older hospitalized patients to predict a panel of post-discharge outcomes.Methods: In this population-based retrospective cohort study, Adults 65+ who experienced an acute hospitalization between 4 November 2008 and 16 March 2016 in Ontario, Canada and received an assessment of functional status at discharge using the Health Outcomes for Better Information and Care (HOBIC) tool were included. Multivariable regression analysis was used to determine the relationship between functional status and emergency department (ED) presentation, hospital readmission, long term care facility (LTCF) admission or wait listing (‘LTCF readiness’), and death at 180 days from discharge.Results: A total of 80,020 patients were included. 38,928 (48.6%) re-presented to the ED, 24,222 (30.3%) were re-admitted, 5,037 (6.3%) were LTCF ready, and 9,047 (11.3%) died at 180 days. Beyond age, functional status at discharge was the factor most associated with LTCF readiness (Odds Ratio [OR] 4.11; 95% Confidence Interval [CI]: 3.70-4.57) and death (OR 3.99; 95% CI: 3.67-4.35). Functional status also had a graded relationship with each outcome and improved the discriminability of the models predicting death and LTCF readiness (p<0.01) but not ED re-presentation or hospital re-admission.Conclusion: Routinely collected functional status at discharge meaningfully improves models to predict long term care home readiness and death. The routine assessment of functional status can inform post-discharge care and service planning for older adults.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".