Prehospital Airway Management for Trauma Patients by First Responders in Six Sub‐Saharan African Countries and Five Other Low‐ and Middle‐Income Countries: A Scoping Review
Bibliographic record
Abstract
The global injury burden disproportionately affecting low- and middle-income countries (LMICs) is exacerbated by a lack of robust emergency medical services. Though airway management (AM) is an essential component of prehospital emergency care, the current standard of prehospital AM training and resources for first responders in LMICs is unknown. This scoping review includes articles published between January 2000 and June 2021, identified using PMC, MEDLINE, and SCOPUS databases, following PRISMA-ScR guidelines. Inclusion criteria spanned programs training formal or informal prehospital first responders. Included articles were assessed for quality using the Newcastle-Ottawa scale. Relevant characteristics were extracted by multiple authors to assess prehospital AM training. Of the initial 713 articles, 17 met inclusion criteria, representing 11 countries. Basic AM curricula were found in 11 studies and advanced AM curricula were found in nine studies. 35.3% (n = 6) of first responder programs provided no equipment to basic life support (BLS) AM training participants, reporting a median cost of $7.00USD per responder trained. Median frequency of prehospital AM intervention was reported in 31.0% (IQR: 6.0, 50.0) of patient encounters (advanced life support trainees: 12.1%, BLS trainees: 32.0%). In three studies, adverse event frequencies during intubation occurred with a median frequency of 22.0% (IQR: 21.0, 22.0). The training deficit in advanced AM interventions in LMICs suggests BLS AM courses should be prioritized, especially in sub-Saharan Africa. Prehospital AM resources are sparse and should be a priority for future development.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".