Descriptive Analysis on ICU Medical Risk Management in United Kingdom,United States,Australia,Canada and Taiwan
Bibliographic record
Abstract
Objective To analyze the policy and guideline,the institutional management and the operation mechanism of ICU medical risk management in the United Kingdom,the United States,Australia,Canada and Taiwan,so as to provide evidence and recommendations for health care risk management policy in China.Methods Such databases as PubMed,EMBASE,The Cochrane Library were searched to include the literatures such as the guideline documents and the research reports on ICU medical risk management in the United Kingdom,the United States,Australia,Canada and Taiwan;the institutional management and the operation mechanism of the risk management in the above four countries and one area were comprehensively analyzed,and especially the UK model was highly emphasized.Results A total of 31 literatures were included,including 1 guideline,5 reviews,2 investigative reports and 23 research documents.The United Kingdom guided the ICU risk management in forms of the standard and the guideline,formulated a clear tool of event classification and corresponding response mechanism.The United States learned from Australia's experience and established the ICU safety reporting system;both of them regarded ICU as one part of the medical risk management and set up a special management column.Conclusion The ICU risk management with the independent report system in the United Kingdom is brought into the scope of national patient safety management,and is regarded as the relative complete system at present.In Australia and the USA,the national institutions are in charge of setting up the research projects of ICU risk management;the industry associations and the non-governmental organizations lead the risk research;and the experimental units popularize gradually after self-application.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".