Anticoagulant therapies for acute venous thromboembolism: a comparison between those discharged directly from the emergency department versus hospital in two Canadian cities
Bibliographic record
Abstract
OBJECTIVE: To compare the characteristics/management of acute venous thromboembolism (VTE) for patients either discharged directly from the emergency department (ED) or hospitalised throughout a year within two urban cities in Canada. DESIGN: Retrospective medical record review. SETTING: Hospitals in Edmonton, Alberta (n=4) and Regina, Saskatchewan (n=2) from April 2014 to March 2015. PARTICIPANTS: All patients discharged from the ED or hospital with acute deep vein thrombosis or pulmonary embolism (PE). Those having another indication for anticoagulant therapy, pregnant/breast feeding or anticipated lifespan <3 months were excluded. PRIMARY AND SECONDARY OUTCOMES: Primarily, to compare proportion of patients receiving traditional therapy (parenteral anticoagulant±warfarin) relative to a direct oral anticoagulant (DOAC) between the two cohorts. Secondarily, to assess differences with therapy selected based on clot burden and follow-up plans postdischarge. RESULTS: 387 (25.2%) and 665 (72.5%) patients from the ED and hospital cohorts, respectively, were included. Compared with the ED cohort, those hospitalised were older (57.3 and 64.5 years; p<0.0001), more likely to have PE (35.7% vs 83.8%) with a simplified Pulmonary Embolism Severity Index (sPESI) ≥1 (31.2% vs 65.2%), cancer (14.7% and 22.3%; p=0.003) and pulmonary disease (10.1% and 20.6%; p<0.0001). For the ED and hospital cohorts, similar proportions of patients were prescribed traditional therapies (72.6% and 71.1%) and a DOAC (25.8% and 27.4%, respectively). For the ED cohort, DOAC use was similar between those with a sPESI score of 0 and ≥1 (35.1% and 34.9%, p=0.98) whereas for those hospitalised lower risk patients were more likely to receive a DOAC (31.4% and 23.8%, p<0.055). Follow-up was most common with family physicians for those hospitalised (51.5%), while specialists/VTE clinic was most common for those directly discharged from the ED (50.6%). CONCLUSIONS: Traditional and DOAC therapies were proportionately similar between the ED and hospitalised cohorts, despite clear differences in patient populations and follow-up patterns in the community.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".