Temporary Intravascular Shunts: When Are We Really Using Them According to the NTDB?
Bibliographic record
Abstract
Temporary intravascular shunts (TIVS) are synthetic intraluminal conduits that maintain arterial and/or venous blood flow. This technique can be used for: 1) replantation; 2) open extremity fractures with extensive soft tissue and arterial injuries; or 3) damage control (extremity/truncal). The literature defining TIVS is composed exclusively of small case series (primarily penetrating injuries). Our goal was to identify the injured population who actually undergoes TIVS using the National Trauma Data Bank (2001 to 2005). TIVS were placed in 395 patients (mean Injury Severity Score = 26; initial hemodynamic instability = 24%; mean based deficit = -7.2; mortality = 14%). Blunt mechanisms caused 64 per cent (251 of 395) of cases. Penetrating injuries were primarily gunshot wounds (97%). Concurrent severe extremity fractures and/or soft tissue defects were present in 185 (74%) blunt-injured patients. Only six of 111 centers performing TIVS used this technique five or more times. Only three centers used TIVS more than 10 times. The volume of TIVS use was similar across the study period (P > 0.05). TIVS is primarily used in blunt motor vehicle collision trauma with concurrent severe extremity fractures and soft tissue injuries. This provides distal perfusion while surgeons assess/fixate the limb. TIVS are placed relatively uncommonly by a large number of trauma centers with a few hospitals using them much more frequently for penetrating injuries.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".