Establishing a core outcomes set for massive transfusion: An Eastern Association for the Surgery of Trauma modified Delphi method consensus study
Bibliographic record
Abstract
BACKGROUND: The management of severe hemorrhage has changed significantly over recent decades, resulting in a heterogeneous description of diagnosis, treatment, and outcomes in the literature, which is not suitable for data pooling. Therefore, we sought to develop a core outcome set (COS) to help guide future massive transfusion (MT) research and overcome the challenge of heterogeneous outcomes reporting. METHODS: Massive transfusion content experts were invited to participate in a modified Delphi study. For Round 1, participants submitted a list of proposed core outcomes. In subsequent rounds, panelists used a 9-point Likert scale to score proposed outcomes for importance. Core outcomes consensus was defined as >85% of scores receiving 7 to 9 and <15% of scores receiving 1 to 3. Feedback and aggregate data were shared between rounds. RESULTS: From an initial panel of 16 experts, 12 (75%) completed three rounds of deliberation to reevaluate variables not achieving predefined consensus criteria. A total of 64 items were considered, with 4 items achieving consensus for inclusion as core outcomes: blood products received in the first 6 hours, 6-hour mortality, time to mortality, and 24-hour mortality. CONCLUSION: Through an iterative survey consensus process, content experts have defined a COS to guide future MT research. This COS will be a valuable tool for researchers seeking to perform new MT research and will allow future trials to generate data that can be used in pooled analyses with enhanced statistical power. LEVEL OF EVIDENCE: Diagnostic Test or Criteria; Level V.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".