Surveillance or no surveillance for deep venous thrombosis and outcomes of critically ill patients
Bibliographic record
Abstract
OBJECTIVE: Surveillance ultrasounds in critically ill patients detect many deep venous thrombi (DVTs) that would otherwise go unnoticed. However, the impact of surveillance for DVT on mortality among critically ill patients remains unclear. DESIGN: We are conducting a multicenter, multinational randomized controlled trial that examines the effectiveness of adjunct intermittent pneumatic compression use with pharmacologic thromboprophylaxis compared to pharmacologic thromboprophylaxis alone on the incidence of proximal lower extremity DVT in critically ill patients (the PREVENT trial). Enrolled patients undergo twice weekly surveillance ultrasounds of the lower extremities as part of the study procedures. We plan to compare enrolled patients who have surveillance ultrasounds to patients who meet the eligibility criteria but are not enrolled (eligible non-enrolled patients) and only who will have ultrasounds performed at the clinical team's discretion. We hypothesize that twice-weekly ultrasound surveillance for DVT in critically ill patients who are receiving thromboprophylaxis will have more DVTs detected, and consequently, fewer pulmonary emboli and lower all-cause 90-day mortality. DISCUSSION: We developed a detailed a priori plan to guide the analysis of the proposed study and enhance the validity of its results.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".