Practices in Triage and Transfer of Critically Ill Patients: A Qualitative Systematic Review of Selection Criteria
Bibliographic record
Abstract
OBJECTIVES: To identify and appraise articles describing criteria used to prioritize or withhold a critical care admission. DATA SOURCES: PubMed, Embase, Medline, EBM Reviews, and CINAHL Complete databases. Gray literature searches and a manual review of references were also performed. Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines were followed. STUDY SELECTION: We sought all articles and abstracts of original research as well as local, provincial, or national policies on the topic of ICU resource allocation. We excluded studies whose population of interest was neonatal, pediatric, trauma, or noncritically ill. Screening of 6,633 citations was conducted. DATA EXTRACTION: Triage and/or transport criteria were extracted, based on type of article, methodology, publication year, and country. An appraisal scale was developed to assess the quality of identified articles. We also developed a robustness score to further appraise the robustness of the evidence supporting each criterion. Finally, all criteria were extracted, evaluated, and grouped by theme. DATA SYNTHESIS: One-hundred twenty-nine articles were included. These were mainly original research (34%), guidelines (26%), and reviews (21%). Among them, we identified 200 unique triage and transport criteria. Most articles highlighted an exclusion (71%) rather than a prioritization mechanism (17%). Very few articles pertained to transport of critically ill patients (4%). Criteria were classified in one of four emerging themes: patient, condition, physician, and context. The majority of criteria used were nonspecific. No study prospectively evaluated the implementation of its cited criteria. CONCLUSIONS: This systematic review identified 200 criteria classified within four themes that may be included when devising triage programs including the coronavirus disease 2019 pandemic. We identified significant knowledge gaps where research would assist in improving existing triage criteria and guidelines, aiming to decrease arbitrary decisions and variability.
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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 | 0.128 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.037 | 0.027 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".