Impact and analysis of the utilization of antibacterial drugs for special use in a first-class hospital with special rectification activities of antibacterials
Bibliographic record
Abstract
Objective: To investigate the implementation effect of special rectification activities on use of special level antibacterial drugs in a first-class hospital from 2011 to the first quarter of 2013. Methods: The application of antibacterials and special level antibacterial drugs and relevant indicators were statistically analyzed by the method of defined daily dose(DDD).Results: Through special rectification activities, the rate of consumption of special level antibacterial drugs in antibacterials amount fell from 39.76% in the first quarter of 2011 to 24.56% in the first quarter of 2013. After the second quarter of 2012, the utilization rate of antibacterials was not more than 60.00%, and the rate of use of special level antibacterial drugs was controlled below 5.40%.The use intensity of antibacterial drugs was 39.8 DDDs per 100 patients in the fourth quarter of 2012. The use of special antibacterial drugs in this hospital was basically rational. Conclusion: Through the special rectification activities, the rational use of special level antibacterial drugs was normalized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".