Independent early predictors of mortality in polytrauma patients: a prospective, observational, longitudinal study
Bibliographic record
Abstract
OBJECTIVES:: Trauma is an important public health issue and associated with substantial socioeconomic impacts and major adverse clinical outcomes. No single study has previously investigated the predictors of mortality across all stages of care (pre-hospital, emergency room, surgical center and intensive care unit) in a general trauma population. This study was designed to identify early predictors of mortality in severely injured polytrauma patients across all stages of care to provide a better understanding of the physiologic changes and mechanisms by which to improve care in this population. METHODS:: A longitudinal, prospective, observational study was conducted between 2010 and 2013 in São Paulo, Brazil. Patients submitted to high-energy trauma were included. Exclusion criteria were as follows: injury severity score <16, <18 years old or insufficient data. Clinical and laboratory data were collected at four time points: pre-hospital, emergency room, and 3 and 24 hours after hospital admission. The primary outcome assessed was mortality within 30 days. Data were analyzed using tests of association as appropriate, nonparametric analysis of variance and generalized estimating equation analysis (p<0.05). ClinicalTrials.gov: NCT01669577. RESULTS:: Two hundred patients were included. Independent early predictors of mortality were as follows: arterial hemoglobin oxygen saturation (p<0.001), diastolic blood pressure (p<0.001), lactate level (p<0.001), Glasgow Coma Scale score (p<0.001), infused crystalloid volume (p<0.015) and presence of traumatic brain injury (p<0.001). CONCLUSION:: Our results suggest that arterial hemoglobin oxygen saturation, diastolic blood pressure, lactate level, Glasgow Coma Scale, infused crystalloid volume and presence of traumatic brain injury are independent early mortality predictors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".