DHOW2 score leads to significant improvement in acute stroke care management emergency department: a prospective analysis
Bibliographic record
Abstract
Background Delays in transfer of patients from emergency department (ED) to stroke ward increases medical complications. We evaluate if a new risk-score ‘DHOW2’ (dysphagia, hemiplegia, observation-required, wet (incontinence) and weight) will identify high-risk patients and whether expedited admission of ‘high-DHOW2’ score patients to SW will result in fewer complications. Methods The DHOW2 score was designed to determine risk of complications following acute stroke. Phase I (279 patients) tested rates of complications with increasing DHOW2. Phase II (1091 patients), evaluated if early admission to the SW of high-DHOW2 patients will lead to fewer complications. Phase III (1257 patients) monitored implementation of the DHOW2 following completion of the study. Findings Medical complications increased with higher-DHOW2 scores during all three phases; 0%–0.8% with DHOW2 of ≤3, 3.1%–6.5% with DHOW2 of 4–5 and 10.9%–14.1% with DHOW2 of ≥6 (p=<0.001). In phase II, more high-DHOW2 patients were admitted expeditiously to the SW from ED resulting in fewer complications, and fewer deaths. The odds of medical complications with DHOW2 of ≥6 was 36.8–58.3 compared with DHOW2 of ≤3. Expedited SW admission of ‘high-DHOW2 patients’ to within 8 hours reduced the development of complications to odds of 19.18–30.17 (p<0.001). Interpretations The DHOW2 score detects patients at risk of AS related medical complications. It is easy to implement in busy EDs where nurses can use the score to identify such patients. The risk stratification by DHOW2 and early transfer of high-scoring patients to SW is associated with significantly fewer complications.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".