Scoping Review of Triage Modifications to Emergency Medical Care in Hospitals Post-COVID-19
Bibliographic record
Abstract
Post-COVID-19, significant triage modifications were made in emergency hospital medical care. Previous scoping reviews investigated triage changes during COVID-19. This scoping review uniquely considers post-pandemic effects. It searches the parameters “COVID-19, triage, hospital, emergency medical care” in four primary databases, one register, and a supplementary database to determine the range of emergency hospital triage changes. Following PRISMA guidelines, studies included are post-2023 publications, those in English, and research studies. Excluded were duplicates, reviews, books, and reports lacking research studies or including irrelevant information on COVID-19, triage, hospital, or emergency medical care. Identified are 1071 records: OVID (n = 20), PubMed (n = 2), Scopus (n = 46), Web of Science (n = 20), Cochrane COVID-19 Register (n = 18), and Google Scholar (n = 965). Six studies are included from the Web of Science (n = 1) and Google Scholar (n = 5). One study includes reports from six different countries; thus, there are 11 reports. The modification of triage was concerning four ways, with each country focusing on a specific triage change. Adaptive changes were proactive rather than reactive. Triage-related future research suggestions include the four triage aspects, international comparisons, and longitudinal change. The recommendation is for research assessing Google Scholar.
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 | 0.035 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".