Length of Stay and Home Discharge for Patients with Inpatient Stroke Rehabilitation
Bibliographic record
Abstract
OBJECTIVE: To examine temporal trends and geographic variations and predict inpatient rehabilitation (IPR) length of stay (LOS) and home discharge for stroke patients. METHODS: Patients aged ≥18 years who were admitted to an IPR facility in Alberta, Canada, between 04/2014 and 03/2018 (years 2014-2017) were included. Predictors of LOS and home discharge were examined using 2014-2016 data and validated using 2017 data. Multivariable linear regression (MLR), multivariable negative binomial (MNB), and multivariable quantile regressions (MQR) were used to examine LOS, and logistic regression was used for home discharge. RESULTS: We included 2686 rehabilitation admissions between 2014 and 2017. The mean LOS decreased (2014: 71 days; 2017: 62.1 days; p = 0.003) during the study period and was shortest in Edmonton (59.1 days) compared to Calgary (66 days) or other localities (70.8 days; p < 0.001). Three-quarters of patients were discharged home and this proportion remained unchanged between 2014 and 2017. Calgary patients were more likely to be discharged home than those in Edmonton (OR = 0.62; p = 0.019) or other localities (OR = 0.39; p = 0.011). The MLR and MNB models provided accurate prediction for the mean LOS (predicted = 59.9 and 60.8 days, respectively, vs. actual = 62.1 days; both p > 0.5), while the MQR model did so for the median LOS (predicted = 44.3 days vs. actual = 44 days; p = 0.09). The logistic regression resulted in 82.4% of correct prediction, a sensitivity of 91.6%, and a specificity of 50.7% for home discharge. CONCLUSIONS: Rehabilitation LOS decreased while the proportion of home discharge remained unchanged during the study period. Both varied across health zones. Identifiable statistical models provided accurate prediction with a separate patient cohort.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".