Interfacility Transport of Critically Ill Patients
Bibliographic record
Abstract
OBJECTIVES: To assess recent advances in interfacility critical care transport. DATA SOURCES: PubMed English language publications plus chapters and professional organization publications. STUDY SELECTION: Manuscripts including practice manuals and standard (1990-2021) focused on interfacility transport of critically ill patients. DATA EXTRACTION: Review of society guidelines, legislative requirements, objective measures of outcomes, and transport practice standards occurred in work groups assessing definitions and foundations of interfacility transport, transport team composition, and transport specific considerations. Qualitative analysis was performed to characterize current science regarding interfacility transport. DATA SYNTHESIS: The Task Force conducted an integrative review of 496 manuscripts combined with 120 from the authors' collections including nonpeer reviewed publications. After title and abstract screening, 40 underwent full-text review, of which 21 remained for qualitative synthesis. CONCLUSIONS: Since 2004, there have been numerous advances in critical care interfacility transport. Clinical deterioration may be mitigated by appropriate patient selection, pretransport optimization, and transport by a well-resourced team and vehicle. There remains a dearth of high-quality controlled studies, but notable advances in monitoring, en route management, transport modality (air vs ground), as well as team composition and training serve as foundations for future inquiry. Guidance from professional organizations remains uncoupled from enforceable regulations, impeding standardization of transport program quality assessment and verification.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".