The Ethical Triage and Management Guidelines of the Entrapped and Mangled Extremity in Resource Scarce Environments: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: A systematic literature review (SLR) was performed to elucidate the current triage and treatment of an entrapped or mangled extremity in resource scarce environments (RSEs). METHODS: A lead researcher followed the search strategy following inclusion and exclusion criteria. A first reviewer (FR) was randomly assigned sources. One of the 2 lead researchers was the second reviewer (SR). Each determined the level of evidence (LOE) and quality of evidence (QE) from each source. Any differing opinions between the FR and SR were discussed between them, and if differing opinions remained, then a third reviewer (the other lead researcher) discussed the article until a consensus was reached. The final opinion of each article was entered for analysis. RESULTS: Fifty-eight (58) articles were entered into the final study. There was 1 study determined to be LOE 1, 29 LOE 2, and 28 LOE 3, with 15 determined to achieve QE 1, 37 QE 2, and 6 QE 3. CONCLUSION: This SLR showed that there is a lack of studies producing strong evidence to support the triage and treatment of the mangled extremity in RSE. Therefore, a Delphi process is suggested to adapt and modify current civilian and military triage and treatment guidelines to the RSE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.061 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".