Management of Coagulopathy in the Patients With Multiple Injuries: Results From an International Survey of Clinical Practice
Bibliographic record
Abstract
BACKGROUND: Bleeding is one of the leading causes of preventable death after traumatic injury. Trauma-associated coagulopathy complicates the control of bleeding. The published approaches on the management of this coagulopathy differ significantly. METHODS: A qualitative international survey of clinical practice among senior physicians responsible for the treatment of patients with multiple injuries (Injury Severity Score > or = 16) was conducted to document common practices, highlight the variabilities, and profile the rationale behind existing clinical practices around the world. RESULTS: Survey results are based on 80 (32%) completed returns, representing 25 countries with 93% of respondents employed by trauma centers and a mean of 20 years clinical experience. There are regional differences in the clinical specialty of physicians responsible for trauma management decisions. Blood loss, temperature, pH, platelets, prothrombin time/INR/activated partial thromboplastin time, and overall clinical assessment, were the most common criteria used to assess coagulopathy. Forty-five percent of respondents claimed to follow a massive transfusion protocol in their institution, 19% reported inconsistent protocol use and 34% do not use a protocol. The management of hypothermia, acidosis, blood products, and adjuvant therapy showed regional as well as institutional variability, and surprisingly few massive transfusion protocols specifically address these issues. CONCLUSIONS: The results of this survey may serve to draw attention to the need for a common definition of coagulopathy and standardized clinical protocols to ensure optimal patient care.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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".