Organ donation in trauma victims: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Although trauma patients represent a large pool of potential organ donors (PODs), the donor conversion rates (DCRs) in this population are unclear. Our primary objective was to synthesize published evidence on DCRs in trauma patients. As a secondary objective, we investigated factors that affect organ donation (OD) in the trauma population. METHODS: We searched four electronic databases (PubMed, Embase, Web of Science, and Cochrane Library) and gray literature for articles on OD in trauma patients (PROSPERO 2017: CRD42017070388). Articles were excluded if it was not possible to calculate the DCR (actual organ donors divided by PODs). We pooled DCRs and performed subgroups analysis by trauma subpopulation, patients' age, and study publication date. RESULTS: We identified 27 articles with a total of 123,142 participants. Cohorts ranged in size from 28 to 120,512 patients (median, 132), with most studies performed in the United States. Conversion rates among individual studies ranged from 14.0% to 75.2% (median, 49.3%). All 27 studies were included in the meta-analysis. We found a pooled DCR of 48.1% using the random effects model. There was a high level of heterogeneity between studies (I = 97.4%). Upon subgroup analysis, we found DCRs were higher in head trauma patients compared with traumatic cardiac arrest patients (45.3% vs 20.9%, p < 0.001), in pediatric patients compared with adults (61.0% vs 38.0%, p = 0.018), and in studies published after 2007 compared with those published before (50.8% vs 43.9%, p < 0.001). Few studies assessed for factors associated with OD in trauma patients. CONCLUSIONS: We found variation in DCRs among trauma patients (range, 14.0-75.2%) and estimated a pooled DCR of 48.1%. Our results are limited by heterogeneity across studies, which may be attributable to differences in study design and population, definitions of a POD, and in the institutional criteria and processes regarding OD. LEVEL OF EVIDENCE: Systematic reviews and meta-analyses level III.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".