Prehospital characteristics that identify major trauma patients: A hybrid systematic review protocol
Bibliographic record
Abstract
<ns3:p>Background International evidence has demonstrated significant improvements both in the trauma care process and outcomes for patients through re-configuring care services from that which is fragmented to integrated trauma networks. A backbone of any trauma network is a trauma triage tool. This is necessary to support paramedic staff in identifying major trauma patients based on prehospital characteristics. However, there is no consensus on an optimal triage tool and with that, no consensus on the minimum criteria for prehospital identification of major trauma. Objective Examine the prehospital characteristics applied in the international literature to identify major trauma patients. Methods To ensure the systematic review is both as comprehensive and complete as possible, we will apply a hybrid overview of reviews approach in accordance with best practice guidelines. Searches will be conducted in Pubmed (Ovid MEDLINE), Embase, Cochrane Library of Systematic Reviews and Cochrane Central Register of Clinical Trials. We will search for papers that analyse prehospital characteristics applied in trauma triage tools that identify major trauma patients. These papers will be all systematic reviews in the area, not limited by year of publication, supplemented with an updated search of original papers from November 2019. Duplication screening of all articles will be conducted by two reviewers and a third reviewer to arbitrate disputes. Data will be extracted using a pre-defined data extraction form, and quality appraised by the Newcastle Ottawa Quality Assessment form. Conclusions An exhaustive search for both systematic reviews and original papers will identify the range of tools developed in the international literature and, importantly, the prehospital characteristics that have been applied to identify major trauma patients. The findings of this review will inform the development of a national clinical prediction rule for triage of major trauma patients.</ns3:p>
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".