Predictors of Hospitalization Among Newly Admitted Skilled Nursing Facility Residents: Rethinking the Role of Functional Decline
Bibliographic record
Abstract
Purpose: Hospital transfer from a skilled nursing facility (SNF) is costly, and many are potentially preventable. This study examines: 1) whether functional decline is a predictor of hospital transfer, and 2) the magnitude of relationships between predictors (functional impairment and chronic medical illness) and hospital transfer from SNFs. Methods: We used Minimum Data Set (MDS) Version 2.0 in the state of Michigan between 2007 and 2009. In total, 196,662 new SNF admissions were observed. Multilevel generalized estimating equations and regression models were performed for each functional and clinical domain while adjusting for demographic variables and change in activities of daily living (ADL). Results: 65% of recently admitted SNF residents experienced functional decline after SNF admission, and 58% were readmitted to a hospital. Residents who needed extensive assistance or were completely dependent in their functional domains had pressure ulcers, deteriorated mood or lower cognitive performance scale scores. These residents experienced higher chances of hospital transfer. However, a deteriorated ADL played a significant role in all multivariate models, indicating that a decline in ADL is a stronger predictor of hospital transfer than other functional or clinical predictors. Conclusion: Although all functional impairments and chronic medical illness can be associated with hospital transfer, functional decline may be the most important predictor of hospital transfer in patients newly admitted to an SNF.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".