Bibliographic record
Abstract
Objective:To standardize drug return in the medical treatment area and to reduce the rate of drug return after leaving the dispensary counter,so as to ensure the quality control of drugs.Methods:The process of drug return was standardized and optimized in the inpatient dispensary of Changhai Hospital,Second Military Medical University.The status of drug return before optimization(in the first quarter of 2012)and after optimization(in the first quarter of 2013)was investigated and compared accordingly.Results:In the first quarter of 2012,there were 2 466drug returns(0.9%),of which 592(24.0%) were the returns before leaving the counter,and 1 874(76.0%)were the returns after leaving the counter.In the first quarter of 2013,there were altogether 1 282drug returns(0.8%),of which 607(47.3%)were the returns before leaving the counter,and 675(52.7%)were the returns after leaving the counter.Chi-square test indicated that significant differences could be noted in the number of drug returns(both the drug returns before leaving the counter and the drug returns after leaving the counter),when the number of drug returns in the first quarter of 2012was compared with that in the first quarter of 2013(P0.01).Conclusion:Process management used in the drug return management of the medical treatment area could effectively improve drug quality management of inpatient dispensaries,and was helpful to the fostering of comprehensive qualities of medical personnel.For this reason,it was worth popularization.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".