The Diagnostic Performance of a Non-Emergency Department-Based Deep Vein Thrombosis Ambulatory Care Pathway
Bibliographic record
Abstract
Background Ambulatory management of isolated acute deep vein thrombosis (DVT) is the standard of care in most patient populations, and in clinical practice is mostly initiated in the emergency department (ED). However, patients referred to the ED with suspected DVT often experience unnecessary delays to diagnosis and subsequent long stays to determine appropriate treatment and follow up care. We implemented a DVT ambulatory care pathway that does not include the ED for referred patients with suspected isolated DVT that begins with pre-test risk stratification in the community and subsequent algorithm-based diagnostic work-up, treatment, and follow-up at a tertiary care centre in Montreal Canada. Objective To determine the diagnostic performance of a non-ED based DVT ambulatory care pathway. Methods We determined the prevalence of DVT over a 46 week period between August 2018 and June 2019 among ambulatory patients with suspected isolated DVT who following community-based risk stratification using the modified Well's clinical prediction score were referred for diagnostic work up and treatment using a newly implemented non-ED based DVT ambulatory care pathway. Results Among 122 patients referred by community physicians, 86 (70%) met pre-defined pathway criteria for assessment of suspected DVT. In all, 42 (49%) were referred with an unlikely/low modified Well's score and 44 (51%) with a likely/high score. Overall, the prevalence of DVT was 19.8%, specifically 9.5% in the unlikely/low and 30.2% in the likely/high pretest probability groups, respectively. Conclusion Our results show that the diagnostic performance of a non-ED based acute DVT ambulatory care pathway is in line with literature estimates. The advantage of this pathway is that it offers clear, evidence-based guidance for community physicians to diagnosis and treat patients in an ambulatory setting without using the ED. The approach is likely to result in both healthcare and economic benefits, including increased patient satisfaction and shorter ED stays. Disclosures Tagalakis: Sanofi Aventis: Other: investigator initiated grant;participated on ad boards; Pfizer: Other: participated on ad boards; BMS-Pfizer: Other: participated on ad boards; Servier: Other: participated on ad boards; Bayer: Other: participated on ad boards.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".