Impact of Diabetes, Hypertension and Heart Failure on Stroke Rehabilitation Care
Bibliographic record
Abstract
Background: Stroke patients who have multiple comorbidities at inpatient rehabilitation admission might experience poorer outcomes than those without comorbidities. Some differences in outcomes between these two groups may be based on age and type of comorbidity. Materials and Methods: Retrospective administrative data from an inpatient stroke rehabilitation unit in a Southwestern Ontario hospital were examined to determine the independent associations between diabetes, hypertension, and heart failure in stroke patients and rehabilitation length of stay (LOS), functional gains in rehabilitation, and discharge destination. We also examined the associations between CHADS2 score and rehabilitation LOS, functional gains in rehabilitation, and discharge destination. Results: Seven hundred and seven cases of stroke subcategorized as experiencing mild (n = 193), moderate (n = 454), and severe (n = 60) stroke were included in the study. Of these patients, 16.4% (n = 116) had type 2 diabetes, 58.7% (n = 415) had hypertension, and 5.8% (n = 41) had congestive heart failure (CHF) prior to stroke. CHF patients were significantly (p = 0.02) older, had significantly (p = 0.014) lower mean FIM gains and were discharged to residential care facilities compared to non-CHF cases. A higher CHADS2 score was significantly associated with Lower FIM gains and discharge to longer term settings. Conclusion: Significant differences exist in the functional gains and discharge disposition of stroke patients based on age of patient, type of comorbidity in stroke, and CHADS2 score.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".