FACTORS CONTRIBUTING TO DEVIATIONS FROM BEST PRACTICE LENGTH OF STAY TARGETS FOR INPATIENT STROKE REHABILITATION BY PATIENT GROUP
Bibliographic record
Abstract
Methods: For inpatient stroke rehabilitation, the Ministry of Health in Ontario sets length of stay (LOS) targets based on evidence for each of seven Stroke Rehabilitation Patient Groups (RPG). From June 2016 to February 2018 we monitored deviations from the recommended length of stay for all stroke clients admitted to a 26-bed inpatient stroke rehabilitation unit. RPGs classify admitted patients using an algorithm based on age and scores on the Functional Independence Measure, into categories based on burden of care, ranging from mild disability to severe disability.Analysis: 156 admissions were analyzed for number of days deviating from the LOS targets by RPG and reasons for that deviation. Discharge destination was also considered.Results: Cases were identified in which LOS targets were exceeded (patients stayed beyond the recommended time) or when actual LOS was shorter than the recommended target. Patients discharged early had met their rehabilitation goals and these cases resulted in a total of 510 u2018savedu2019 days. While patients exceeding the recommended LOS accounted for a total of 579 additional days in hospital. These patients did so for a variety of reasons. Medical complications and mobility/ADL goals not yet met were the two most common reasons for delay in discharge, with delays caused by discharge planning and goals for dysphagia being the next two most common reasons. Conclusion: Identifying themes contributing to cases in which best practice LOS targets are exceeded will help to identify health services needed in order to proactively address these issues and improve flow.
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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 | 0.005 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".