Analysis of Transfusion Volumes in the Elderly Trauma Population
Bibliographic record
Abstract
Background: Blood transfusion and old age have been independently associated with worse outcomes and higher mortality in the trauma population. Determining specific volumes and ratios of blood products for the elderly population, a special population with varied hemodynamic responses to traumatic injury, is a challenge. This study was aimed at delineating the relationship between specific transfusion volumes and mortality with an ultimate goal of finding an optimal threshold where risk outweighs benefit. Methods: A retrospective study of data from patients aged 65 and older at a level II urban trauma center was conducted. All patients who were included in the study presented to the emergency department between January 2013 and January 2016 and received a blood product transfusion (n = 93). The primary outcome was defined as mortality 24 h after transfusion, while the secondary outcome measured was length of hospital stay. Optimal cut-off points were estimated using Youden J coefficients, and Cox proportional hazard ratio (HR) was performed to calculate mortality risk. Results: Patients receiving less than 5 units of blood product had a shorter length of stay (5.19 days) and decreased risk of mortality (HR = 2.48, P = 0.01). Patients receiving >= 5 units of a blood product had a statistically significant increase in risk of mortality (HR = 6.207, P < 0.001) and length of hospital stay (12.47 days), regardless of injury severity score. The administration of fresh frozen plasma was also an independent predictor of mortality. Patients who received a plasma transfusion had an increased risk of mortality (HR = 3.25, P < 0.001). Conclusion: In this study, a threshold point of greater than 5 units of blood products has been associated with increased mortality and length of hospital stay. A more restrictive transfusion strategy has potential for improved outcomes. J Curr Surg. 2019;9(4):45-50 doi: https://doi.org/10.14740/jcs392
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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 | 0.003 |
| 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 | 0.000 | 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".