The use status of antibiotics and bacterial resistant analysis of inpatients
Bibliographic record
Abstract
Objective To analyze the using status of antibiotics in The Fifth Affiliated Hospital of Xinjiang Medical University of the 1st quarter of 2011 and 2012,and to study the resistance of clinical bacterial isolates to common used antibiotics in our hospital in order to provide reference for clinical rational applications of agents.Methods Retrieved HIS database of our hospital to compare the consumption of antibiotics for the inpatients in the 1st quarter of 2011 and 2012.Sequenced them according to antibiotics Defined Daily Doses(DDDs) and analyze the resistant changes for detected bacteria.Results The top 10 DDDs antibiotics were largely different between the 1st quarter of 2011 and that of 2012 in our hospital,and the clinical using antibiotics primarily were Cephalosporins and Fluoroquinolones.In the 1st quarter of 2011,clinical bacterial isolates included 102 strains of Gram+(10.31%),786 strains of Gram-(79.48%) and 101 strains of Candida Albicans(10.21%) while in that of 2012,it included 120 stains of Gram+(14.90%),624 strains of Gram-(77.52%) and 61 strains of candida albicans(7.58%).The bacterial resistances between the 1st quarter of 2012 and that of 2011 were some fluctuations,and the sensitivities of most drugs on pathogens were somewhat increasing.Conclusion From then on carrying out specific renovating action for clinical uses of antibiotics in our hospital,clinical rational applications level of antibiotics raised so that the bacterial resistance was effectively controlled and the action achieved phased objectives.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".