Predicting rehabilitation length of stay in Canada: It’s not just about impairment
Bibliographic record
Abstract
INTRODUCTION: Current tertiary Spinal Cord Injury (SCI) rehabilitation funding and rehabilitation length of stay (R-LOS) in most North American jurisdictions are linked to an individual's impairment. Our objectives were to: 1) describe the impact of relevant demographic, impairment and medical complexity variables at rehabilitation admission on R-LOS among adult Canadians with traumatic SCI; and 2) identify factors which extend R-LOS. METHODS: Data from 1,376 adults with traumatic SCI were obtained via chart abstraction and administrative data linkage from 15 Rick Hansen SCI Registry sites (2004-2014). Variables included age, sex, neurological impairment (level, severity), rehabilitation onset days, R-LOS, Glasgow Coma Score (GCS) at admission, prior ventilation or endotracheal tube (Vent/ETT), or indwelling bladder catheter at acute discharge, pain interference score, intensive care unit (ICU) length of stay (LOS), and lower extremity motor scores (LEMS) at rehabilitation admission. Variables related to R-LOS in bivariate analysis were included in multivariate analysis to determine their impact on R-LOS. RESULTS: Prior Vent/ETT tube, indwelling bladder catheter, GCS, LEMS, and neurological impairment were related to R-LOS in bivariate analysis. Multivariate linear regression analyses identified five variables as significant predictors: age, Vent/ETT for >24 hours in acute care, indwelling bladder catheter at acute discharge, LEMS, and NLI/AIS subgroup at rehabilitation admission explained 32% of the variation in R-LOS (p<0.001). CONCLUSIONS: Based on the enclosed formula, and knowledge of an individual's age at injury, spinal cord impairment (level and severity), prior Vent/ETT, presence of an indwelling bladder catheter, and LEMS at admission, administrators and clinicians may readily identify patients for whom an extended R-LOS beyond conventional LOS targets is likely.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".