Foreign medical risk management in Department of orthopedics and its development trends
Bibliographic record
Abstract
Objective Introduce the current situation of the United States,Canada,United Kingdom and Australia orthopedics medical risk management,including regulatory agencies,relevant laws,reporting systems and analysis methods,to provide the development trend.Methods Though Wan fang,CNKI database,searched the reviews about the United States,Canada,United Kingdom and Australia orthopedics medical risk management from 2001 to 2011,and accessed to research.Results summed up the methods of the orthopedic medical risk management,then summarized the significance of its worth learning.Conclusion Compared to the domestic system,The United States,Canada,United Kingdom and Australia orthopedics medical risk management were better.These countries attached great importance to the establishment of the system and gradually improved.The government led the orthopedic medical risk management institutions,and laws were passed to support the entire system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".