Pre-stroke disability and stroke severity as predictors of discharge destination from an acute stroke ward
Bibliographic record
Abstract
BACKGROUND AND RATIONALE: Reliable prediction of discharge destination in acute stroke informs discharge planning and can determine the expectations of patients and carers. There is no existing model that does this using routinely collected indices of pre-morbid disability and stroke severity. METHODS: Age, gender, pre-morbid modified Rankin Scale (mRS) and National Institutes of Health Stroke Scale (NIHSS) were gathered prospectively on an acute stroke unit from 1,142 consecutive patients. A multiclass random forest classifier was used to train and validate a model to predict discharge destination. RESULTS: Used alone, the mRS is the strongest predictor of discharge destination. The NIHSS is only predictive when combined with our other variables. The accuracy of the final model was 70.4% overall with a positive predictive value (PPV) and sensitivity of 0.88 and 0.78 for home as the destination, 0.68 and 0.88 for continued inpatient care, 0.7 and 0.53 for community hospital, and 0.5 and 0.18 for death, respectively. CONCLUSION: Pre-stroke disability rather than stroke severity is the strongest predictor of discharge destination, but in combination with other routinely collected data, both can be used as an adjunct by the multidisciplinary team to predict discharge destination in patients with acute stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".