Comparison of the Prognostic Significance of Initial Blood Lactate and Base Deficit in Trauma Patients
Bibliographic record
Abstract
BACKGROUND: Initial blood lactate and base deficit have been shown to be prognostic biomarkers in trauma, but their respective performances have not been compared. METHODS: Blood lactate levels and base deficit were measured at admission in trauma patients in three level 1 trauma centers. This was a retrospective analysis of prospectively acquired data. The association of initial blood lactate and base deficit with mortality was tested using receiver operating characteristics curve, logistic regression using triage scores (Revised Trauma Score and Mechanism Glasgow scale and Arterial Pressure score), and Trauma Related Injury Severity Score as a reference standard. The authors also used a reclassification method. RESULTS: The authors evaluated 1,075 trauma patients (mean age, 39 ± 18 yr, with 90% blunt and 10% penetrating injuries and a mortality of 13%). At admission, blood lactate was elevated in 425 (39%) patients and base deficit was elevated in 725 (67%) patients. Blood lactate was correlated with base deficit (R = 0.54; P < 0.001). Using logistic regression, blood lactate was a better predictor of death than base deficit when considering its additional predictive value to triage scores and Trauma Related Injury Severity Score. This result was confirmed using a reclassification method but only in the subgroup of normotensive patients (n = 745). CONCLUSIONS: Initial blood lactate should be preferred to base deficit as a biologic variable in scoring systems built to assess the initial severity of trauma patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".