Traumatic primary pulmonary thrombosis: injury and treatment patterns of a distinct clinical entity
Bibliographic record
Abstract
BACKGROUND: Traditionally, pulmonary thrombi following trauma were believed to occur secondary to embolization from deep vein thrombosis (DVT). However, computed tomography (CT) during initial trauma resuscitation has identified pulmonary thrombi, which raises the possibility of primary pulmonary thrombosis as a distinct clinical entity. This study identifies cases of pulmonary thrombosis identified immediately after trauma and describes associated injury patterns and treatments. METHODS: We conducted a retrospective review of the trauma and radiology registries at a Canadian level-1 trauma centre from January 2010 to April 2021. A chart review identified patients with pulmonary thrombi on initial CT. We extracted and analyzed patient demographic characteristics, mechanism of injury, summary of injuries, treatments, and outcomes. RESULTS: = 8, 33%) were common. Four patients had a concomitant DVT, and 10 patients did not undergo assessment for DVT; 10 patients were identified as having primary pulmonary thrombosis. Treatment was started in 18 patients (75%): 9 patients were treated with dalteparin, 2 with dalteparin and inferior vena cava (IVC) filter, 6 with IVC filter in isolation, and 1 with IVC filter and intravenous heparin. Five patients (21%) died from their injuries. CONCLUSION: Early pulmonary thrombosis was associated with chest injuries, often without DVT. These findings challenge the traditionally held view of DVT embolization as the cause of pulmonary thrombosis immediately following trauma and suggest that primary pulmonary thrombosis is a distinct clinical entity.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".