The Utility of Screening for Asymptomatic Lower Extremity Deep Venous Thrombosis during Inflammatory Bowel Disease Flares
Bibliographic record
Abstract
BACKGROUND: Asymptomatic deep vein thrombosis (DVT) occurs in up to 11% of medical inpatients. The incidence of asymptomatic DVT among patients with inflammatory bowel disease (IBD) is unknown but may be even higher. D-dimer is effective for DVT screening, but its utility has not been studied in the IBD population. METHODS: Hospitalized and ambulatory patients with IBD during flares were recruited between 2009 and 2011. Those with clinical symptoms of venous thromboembolism or previous venous thromboembolism were excluded. We determined the prevalence of DVT among asymptomatic subjects using lower extremity Doppler ultrasound and assessed the performance characteristics of the D-dimer in this high-risk study population. RESULTS: We enrolled 101 hospitalized and 49 ambulatory patients with IBD during active flares. There were no cases of proximal DVT detected by lower extremity Doppler ultrasound. The 95% confidence interval (CI) for the rate of proximal DVT was 0% to 2%. D-dimer was elevated in 60% of subjects without DVT, occurring more frequently among hospitalized than ambulatory subjects [89% versus 65%, P = 0.01; adjusted odds ratio (aOR), 4.16, 95% CI, 1.58-10.9]. Other predictors of elevated D-dimer were incremental decade in age (aOR, 1.97; 95% CI, 1.24-3.14); ulcerative colitis versus Crohn's disease diagnosis (aOR, 3.38; 95% CI, 1.29-8.84); and every 10-unit increase in C-reactive protein (aOR, 1.33; 95% CI, 1.09-1.62). CONCLUSION: From this pilot study, there appears to be low prevalence of asymptomatic DVTs among patients with IBD during flares. The high prevalence of elevated D-dimer in DVT-negative patients limits its utility in IBD.
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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 | 0.013 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".