Creation of a Rehabilitation Prediction Rule: a Prioritization Procedure
Bibliographic record
Abstract
Older adults may require longer recovery periods prior to being discharged from the hospital after an acute care stay. For some, returning to their previous living arrangement may no longer be safe or feasible after an acute care admission, and they may require alternate levels of care. It can be challenging to evaluate which patients may benefit most from inpatient rehabilitation versus those for whom alternate levels of care are more suitable. Using a prioritization procedure, this study identified and ranked predictive factors for successful inpatient rehabilitation (defined as discharge to previous living arrangement) from most to least important. The final round of the prioritization procedure resulted in a list of the top 20 predictive factors, ranked by health-care providers in the field, from most to least important. Predictive factors included demographic information, past medical history factors, acute care illness factors, and results of investigations performed during the index hospitalization. The top ranked predictive factors related to patients' previous living arrangements, level of independence before hospitalization, and presence or absence of cognitive impairment. The bottom ranked predictive factors related to physical measures and results of inpatient investigations at the time of transfer. These findings highlight the importance of considering patients' lived experiences prior to hospitalization when determining who may obtain the greatest benefit from further, intensive inpatient rehabilitation following an acute care hospitalization.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".