Are IVC Filters Required in Combat Support Hospitals?
Bibliographic record
Abstract
BACKGROUND: Haemorrhagic shock from traumatic injuries is now often treated using a damage control resuscitation strategy that transfuses packed red blood cells, plasma and platelets in a 1:1:1 ratio, early use of activated recombinant factor VII and transfusion of fresh whole blood. These therapies are aimed at promoting thrombosis in injured vessels. Such patients are at high risk for thrombotic complications and thromboprophylaxis is necessary, but frequently impossible to use in the early phase of care. CASE PRESENTATION: We describe the case of an Afghan civilian worker who suffered a vertical shear pelvic fracture with massive bleeding in a pedestrian/truck collision that was treated with a damage control resuscitation strategy, and who later suffered a severe pulmonary embolus. The potential use of a temporary inferior vena cava [IVC] filters is discussed. RECOMMENDATIONS: Care providers and policy makers must recognize that the increased use of prothrombotic strategies of resuscitation will likely increase the incidence of thrombotic complications in the high risk population of severely injured patients in combat support hospitals. Monitoring the incidence of these complications and development of strategies for prevention and treatment are required to avoid undermining the positive outcomes of damage control resuscitation. These strategies could include supplying combat support hospitals with the equipment and training necessary for placement of temporary IVC filters under fluoroscopic guidance.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".