Diagnosing deep vein thrombosis early in critically ill patients (DETECT) trial: a protocol for a randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Deep vein thrombosis (DVT) in critically ill patients is often undetected. However, it is unclear whether ultrasound surveillance for early detection of DVT in high-risk medical-surgical intensive care unit (ICU) patients improves patients' outcomes. The DETECT trial (Diagnosing deep-vein thrombosis early in critically ill patients) evaluates the effect of twice-weekly bilateral lower limb ultrasound compared to usual care on 90-day mortality of critically ill adult patients admitted to medical, surgical and trauma ICUs. METHODS AND ANALYSIS: The DETECT trial is an international, parallel-group, open-label, randomised trial, which will recruit 1800 critically ill adults from over 14 hospitals in Saudi Arabia and Kuwait. Eligible patients will be allocated to twice-weekly bilateral lower limb ultrasound or usual care. The primary outcome is 90-day mortality. Secondary outcomes include lower limb proximal DVT, pulmonary embolism and clinically important bleeding. The first patient was enrolled on 21 March 2023. As of 8 April 2025, 711 patients have been enrolled from 14 centres in Saudi Arabia and Kuwait. The first interim analysis was conducted on 14 May 2025. We expect to complete recruitment by December 2026. ETHICS AND DISSEMINATION: Institutional review boards (IRBs) of each participating institution approved the study. We plan to publish the results in peer-reviewed journals and present the findings at international critical care conferences. TRIAL REGISTRATION NUMBER: Clinicaltrials.gov: NCT05112705, registered on 9-11-2021.
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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 | 0.038 | 0.040 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.079 | 0.014 |
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".