Improving the Diagnostic Process for Deep Vein Thrombosis in Orthopaedic Outpatients
Bibliographic record
Abstract
Prompt diagnosis of proximal lower extremity deep vein thrombosis in outpatients is critical because of the risk of pulmonary embolism. Our purpose was to determine the accuracy of orthopaedists' clinical decisions regarding the diagnosis of proximal deep vein thrombosis in outpatients. A nationally representative random sample of 2300 orthopaedists received a survey of six clinical vignettes. They were asked to estimate the probability of proximal lower extremity deep vein thrombosis using defined criteria and to specify their planned diagnostic tests. A clinical decision rule and evidence-based diagnostic test recommendations from the general literature served as the gold standard for comparison. Six-hundred seventy-six (29%) surgeons completed the survey. The orthopaedists' planned diagnostic tests differed from the gold standard, but these differences varied depending on the probability of deep vein thrombosis. For the moderate and high risk vignettes, the diagnostic test recommendations agreed with the gold standard approximately 70% of the time. With the exception of gender, no differences were found between respondents and nonrespondents. Orthopaedists' approach to the diagnosis of deep vein thrombosis in outpatients potentially could be improved by applying a clinical decision rule and current evidence on diagnostic test usage.
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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 | 0.010 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".