Computerised Tomography for the Detection of Pulmonary Emboli in Intensive Care Patients—a Retrospective Cohort Study
Bibliographic record
Abstract
Pulmonary emboli are frequently considered as a cause for respiratory deterioration in intensive care unit (ICU) patients, however empirical observation suggests that computerised tomographic (CT) angiography is infrequently positive after the first 24 hours. This study aimed to determine the rate and risk factors for detection of pulmonary emboli by CT angiography in ICU patients. All patients undergoing CT angiography > 24 hours after ICU admission for respiratory deterioration from April 2000 until January 2004 were included. The positivity rate for pulmonary emboli was determined and risk factors analysed. Seven (6%) out of 113 CT angiograms were positive for pulmonary emboli. All were found in trauma patients. Comparing positive to negative scans, predefined risk factors including head injury (5/7 positive scans, 71% vs. 23/106 negative scans, 22%, P = 0.005), spine injury with neurological impairment (4/7, 57% vs. 9/106, 8%, P = 0.002) and lower limb injury (3/7, 43% vs. 12/106, 9%, P = 0.039) were significantly more frequent in patients with positive scans. Deep vein thrombosis prophylaxis was employed less frequently prior to a positive scan (in 3/7, 43% patients with positive scans vs. 91/106, 86% patients with negative scans P = 0.015). Only the predefined risk factors were independently associated with positive CT angiography on limited logistic regression (OR 24.7 per risk factor, 95% CI 2.38 to 255.1, P = 0.007). Pulmonary emboli were infrequently diagnosed using CT angiography in ICU patients admitted for more than 24 hours and found only in patients with recognised risk factors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".