The Impact of Alcohol Misuse in Trauma Patients: A Scoping Review Protocol
Bibliographic record
Abstract
Abstract Background Alcohol use is a contributing factor in many cases of traumatic injury. There is conflicting evidence on the impact of alcohol use at the time of physical trauma on severity of injury and hospital course. Similarly, the significance of alcohol use disorder on outcomes in hospitalized trauma patients is unclear. This scoping review aims to provide a concise overview of the current literature surrounding peri-trauma alcohol use and alcohol use disorder on injury severity, in-hospital complications, patient outcomes, and long-term health impact of alcohol use in trauma. We will also explore the associated healthcare costs of this patient population. Methods A systematic search of the following databases MEDLINE, EMBASE, and Cochrane Library will be completed to extract all studies that meet our inclusion criteria from January 2000 onwards. Case reports will be excluded. Two reviewers will screen all citations, abstracts, and full text articles. A third reviewer will act as tiebreaker at each stage of the screening process. A narrative synthesis without meta-analysis will be conducted and assessed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Discussion This review will contribute to the literature by providing a concise overview of the current data on the impact of alcohol on outcomes following trauma. We will explore the overall themes in the literature, limitations, and future directions to focus forthcoming research in this patient population. Scoping review registration Open Science Framework ( https://doi.org/10.17605/OSF.IO/Z84WK ) Support There were no funders or sponsors involved in the development of this protocol
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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 | 0.102 | 0.094 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.018 | 0.016 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.084 | 0.014 |
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".