Association of the neutrophil–lymphocyte ratio to patient outcomes after trauma: A systematic review
Bibliographic record
Abstract
Introduction Early identification of trauma injury severity is important for prognostication. The neutrophil–lymphocyte ratio (NLR) has been proposed as a marker of systemic inflammation in major trauma patients that is associated with in-hospital mortality. The aim of this systematic review is to compile all the best evidence available to determine the prognostic capabilities of the NLR in trauma and to assess the NLR as a predictor of mortality in adult major trauma patients. Additionally, comparing NLR and hospital length of stay (LOS), ICU LOS, mechanical ventilation and transfusion requirements. Methods We conducted a search of online information sources to identify manuscripts observing the NLR in adult major trauma patients. Outcomes of interest include mortality as defined by the author, hospital LOS, ICU LOS, mechanical ventilation and transfusion requirements. Quality was assessed using the Newcastle–Ottawa Scale. We aimed to conduct a meta-analysis if there were sufficient manuscripts included. Results Eight studies fulfilled our inclusion criteria. Trials were of good methodological quality. Substantial heterogeneity present between the studies prevented a meta-analysis from being conducted. Overall, five studies demonstrated the NLR as a significantly predictive marker of mortality. NLR was observed to be significantly associated with increased ICU LOS and longer duration of mechanical ventilation. Mixed results were observed between NLR and hospital LOS and transfusion requirements. Conclusions A potential association between NLR and mortality, ICU LOS and duration of mechanical ventilation has been reported. However, clinical utility of this measure during trauma resuscitation remains unknown.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".