Apply the systematic risk management—AS/NZS 4360:2004 to operate the project of preoperative evaluation
Bibliographic record
Abstract
Objective: Currently, the medical adverse events stem in part from a lack of significant risk management in preoperative evaluation. This study was to apply the systematic risk management —AS/NZS 4360:2004 to perform the project of preoperative evaluation.Methods: With the idea of risk management, the doctor’s classification and surgery’s groups were graded to lay the foundation for project management. Then a preoperative evaluation center was established as a screening role in health management based on AS/NZS 4360:2004.Results: A total of 144 out of 1,436 patients were identified as ones with much risk mainly including clinical characteristics such as abnormal test (n = 27), cardiovascular disease (n = 27) and fever (n = 23) from pediatric (35%), general surgery (20%) and trauma (15.66%) department. Finally, the potential risk was reduced in the medical process meanwhile the quality of treatment was improved.Conclusions: This study shows that risk management could be applied into all aspects of hospital management as a drastic and practiced tool.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".