The Impact of Transferring Stroke Patients: An Analysis of National Administrative Data
Bibliographic record
Abstract
BACKGROUND: Interhospital transfer is an important but resource-intensive pattern of care. The use for stroke patients is highly dependent upon health system structure. We examined the impact of hospital transfers for stroke care in Canada. METHODS: We analyzed hospital administrative data within the Canadian Institute for Health Information (CIHI) Database for the 3 fiscal years 2011/12, 2012/13 and 2013/14. Patients with clinical stroke syndrome (ischemic or hemorrhagic) were identified using International Classification of Diseases. Stroke centers were defined by Heart & Stroke Foundation of Canada stroke report. RESULTS: During the 3-year period,397 patients in Canada (excluding Quebec) were admitted to hospital for clinical stroke syndrome. Median age was 75 (interquartile range [IQR] 64-84) years; 50.6 % were male. Less than 5% (n=4030) of patients were transferred. Patients transferred to stroke centers were younger (p<0.001) and had shorter median length of stay (p<0.001). The highest probability of discharge home was associated with sole care at stroke center (43.8%). Transfer to stroke center from community hospital had the highest probability for discharge to rehabilitation facility (25%) and lowest to either long-term (2.1%) or complex community care (2.0%). Transferred patients had lower mortality at discharge. CONCLUSION: Younger patients were transferred more frequently to stroke centers; older patients were more likely treated in community hospitals. Sole stroke center care was associated with high discharge rate to home; transfer to a stroke center was associated with high discharge rate to rehabilitation and lower mortality rates.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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 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".