Elevated Lactate as a Mortality Factor in Poly Traumatised Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: According to global health estimates from the World Health Organization (WHO) injuries represent 8% of world deaths. There are systematic reviews that relate lactate and mortality in trauma patients but do not focus on multiple trauma patients. Objective: To determine if elevated lactate is a mortality factor in multiple trauma patients. Methodology: A systematic review and meta-analysis of observational studies were carried out. The search was carried out in 4 databases: PUBMED, Embase, Scopus, and Web of Science. Data were pooled using a random effects model and summary statistics were calculated using odds ratios (ORs) with their respective 95% confidence intervals (95% CI). Results: Nine studies were included (n=5302). A significant association was found between elevated admission lactate with mortality (OR: 1.80; 95% CI 1.11 to 2.91) and 72-hour mortality (OR: 1.24; 95% CI 1.02 to 1.50). No statistically significant association was found for the analysis of elevated admission lactate and 28-day mortality (OR: 1.24; 95% CI 1.02 to 1.50). Finally, elevated admission lactate is associated with mortality regardless of time (OR: 1.34; 95% CI 1.19 to 1.50). Conclusion: Elevated admission lactate is associated with mortality and 72-hour mortality in multiple trauma patients. No significant association was found between elevated admission lactate and 30-day mortality. Elevated intake of lactate is associated with mortality independent of time.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".