Understanding traumatic shock
Bibliographic record
Abstract
BACKGROUND: Among trauma patients with out-of-hospital hypotension, we evaluated the predictive value of systolic blood pressure (SBP) with and without other physiologic compromise for identifying trauma patients requiring early critical resources. METHODS: This was a secondary analysis of a prospective cohort of injured patients 13 years or older with out-of-hospital hypotension (SBP ≤ 90 mm Hg) who were transported by 114 emergency medical service agencies to 56 Level I and II trauma centers in 11 regions of the United States and Canada from January 1, 2010, through June 30, 2011. The primary outcome was early critical resource use, defined as blood transfusion of 6 U or greater, major nonorthopedic surgery, interventional radiology, or death within 24 hours. RESULTS: Of 3,337 injured patients with out-of-hospital hypotension, 1,094 (33%) required early critical resources and 1,334 (40%) had serious injury (Injury Severity Score [ISS] ≥ 16). Patients with isolated hypotension required less early critical resources (14% vs. 52%), had less serious injury (20% vs. 61%), and had lower mortality (24 hours, 1% vs. 26%; in-hospital, 3% vs. 34%). The standardized probability of requiring early critical resources was lowest among patients with blunt injury and isolated moderate hypotension (0.12; 95% confidence interval, 0.09-0.15) and steadily increased with additional physiologic compromise, more severe hypotension, and penetrating injury (0.94; 95% confidence interval, 0.90-0.98). CONCLUSION: A minority of trauma patients with isolated out-of-hospital hypotension require early critical resuscitation resources. However, hypotension accompanied by additional physiologic compromise or penetrating injury markedly increases the probability of requiring time-sensitive interventions. LEVEL OF EVIDENCE: Prognostic study, level II.
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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 | 0.007 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.014 |
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".