Factor first! Emergency department management of persons with hemophilia: a single-center study
Bibliographic record
Abstract
BACKGROUND: Timely hemostatic therapy is essential when persons with hemophilia (PwH) present to the emergency department (ED), with guidelines recommending treatment at the time a bleed is suspected ('Factor First' strategy). RESEARCH DESIGN AND METHODS: This study examined ED management of PwH in Southern Alberta from 2014 to 2022, focusing on adherence to guidelines. RESULTS: A total of 393 ED visits from 191 adults with hemophilia A or B were identified. Most visits (86%) required emergent or urgent care. Median times from ED registration to ordering and administering hemostatic therapy were 2.9 and 4.2 hours, respectively, with treatment given within 2 hours in a minority of cases. Laboratory tests were ordered in nearly half of visits, with a median ordering time of 65 minutes. Use of individualized emergency management protocols increased over time, reaching 91.4% in 2022. Median ED stay was 3.4 hours, and approximately 25% of visits had a hemophilia-related admission diagnosis. CONCLUSIONS: These findings highlight persistent delays and nonadherence to best practice recommendations, emphasizing the need for targeted quality improvement interventions to ensure timely, guideline-concordant care for PwH in emergency settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".