Clinically important deep vein thrombosis in the intensive care unit: a survey of intensivists
Bibliographic record
Abstract
INTRODUCTION: Outside the intensive care unit (ICU), clinically important deep vein thrombosis (DVT) is usually defined as a symptomatic event that leads to objective radiologic confirmation and subsequent treatment. The objective of the present survey is to identify the patient factors and radiologic features of lower limb DVT that intensivists consider more or less likely to make a DVT clinically important in ICU patients. METHODS: Our definition of clinically important DVT was a DVT likely to result in short-term or long-term morbidity or mortality if left untreated, as opposed to a DVT that is unlikely to have important consequences. We asked respondents to indicate the likelihood that patient factors and ultrasonographic features make a DVT clinically important using a five-point scale (from 1 = much less likely to 5 = much more likely). RESULTS: Of the 71 Canadian intensivists who responded, 70 (99%) rated three patient factors as most likely to make a DVT clinically important: clinical suspicion of pulmonary embolism (mean score 4.6), acute or chronic cardiopulmonary morbidity that might limit a patient's ability to tolerate pulmonary embolism (score 4.5), and leg symptoms (score 4.2). Of the ultrasound features, proximal (score 4.7), large (score 4.2), and totally occlusive (score 3.9) thrombi were considered the three most important. CONCLUSION: When labeling a DVT as clinically important, intensivists rely on different patient specific factors and thrombus characteristics than are used to assess the clinical importance of DVT outside the ICU. The clinical importance of DVT is influenced by unique factors such as cardiopulmonary reserve among mechanically ventilated patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".