Regionalisation of Intensive Care and Extra-Corporeal Membrane Oxygenation Services in the UK: Beliefs about the Evidence, Benefits and Harm
Bibliographic record
Abstract
Extra-corporeal membrane oxygenation (ECMO) is used as rescue therapy for adults with severe acute respiratory failure. We aimed to determine the views of intensive care clinicians on regionalisation of critical care services and on the development of adult ECMO services in the UK. A survey was undertaken of all members of the UK Intensive Care Society; 2,133 participants were invited to complete the survey and 691 responded (32.7%). Among respondents, 65% believed that adult ECMO services should be expanded, 42.5% agreed that intensive care services in the UK should be regionalised, while 63.8% agreed the UK should develop regional ventilatory care centres including ECMO services. Experience during H1N1 influenza pandemics was the factor respondents most frequently identified as driving ECMO expansion (61.1%). Of respondents, 60.1% believe that an expanded ECMO service should be provided in 5–10 supra-regional centres. Patient safety, resources, guidelines and transportation of sick patients were also seen as important issues. We conclude that there is a reasonable level of support for regionalisation of intensive care services and for expansion in ECMO services for adults with severe acute respiratory failure in the UK. Clinicians support appropriate funding, investment in transport services and the development of national guidelines.
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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 | 0.021 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".