Traumatic Abdominal Solid Organ Injury Patients Might Benefit From Thromboelastography-Guided Blood Component Therapy
Bibliographic record
Abstract
BACKGROUND: Thromboelastography (TEG) has been utilized for the guidance of blood component therapy (BCT). We aimed to investigate the association between emergent TEG-guided BCT and clinical outcomes in patients with traumatic abdominal solid organ (liver and/or spleen) injuries. METHODS: A single center retrospective study of patients who sustained traumatic liver and/or spleen injuries receiving emergent BCT was conducted. TEG was ordered in all these patients. Patient demographics, general injury information, outcomes, BCT, and TEG parameters were analyzed and compared in patients receiving TEG-guided BCT versus those without. RESULTS: A total of 166 patients were enrolled, of whom 52% (86/166) received TEG-guided BCT. A mortality of 12% was noted among patients with TEG-guided BCT when compared with 19% of mortality in patients with non-TEG-guided BCT (P > 0.05). An average of 4 units of packed red blood cell (PRBC) was received in patients with TEG-guided BCT when compared to an average of 9 units of PRBC received in non-TEG-guided BCT patients (P < 0.01). A longer hospital length of stay (LOS, 19 ± 16 days) was found among non-TEG-guided BCT patients when compared to the TEG-guided BCT group (14 ± 12 days, P < 0.05). TEG-guided BCT showed as an independent factor associated with hospital LOS after other variables were adjusted (coefficiency: 5.44, 95% confidence interval: 0.69 - 10.18). CONCLUSIONS: Traumatic abdominal solid organ injury patients receiving blood transfusions might benefit from TEG-guided BCT as indicated by less blood products needed and less hospitalization stay among the cohort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".