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 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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".