Quality of Endoscopic Disinfection in Jiangsu Provincial Second Care Level Hospitals
Bibliographic record
Abstract
OBJECTIVE To understand the effect of the cleaning and disinfection of endoscopes in endoscopic rooms to discover the insufficiency of controlling nosocomial infection.METHODS The biological monitoring of the endoscopes and the situation of endoscopic rooms from fourth quarter 2008 to third quarter 2009 were performed in a retrospective way.The qualified rate of bacterial culture of endoscopes in all provincial second care level hospitals and their relation with the patients′ exclusive time on a high level of consultation were analyzed.RESULTS Totally 72 samples were collected from 6 second care level hospitals.The qualified rate of bacterial culture of endoscopes was 90.27%.The maximal bacteria-carrying capacity of endoscopes was 1600 CFU/per piece.The average quantity of endoscopes and the average working time were 4 pieces and 7.17 hours in each second care level hospitals,respectively.The average max treatment volume was 21.The patients′ exclusive time was 1.31±0.53 hours on a high level of consultation.Bivariate correlation tests indicated that significant correlation exists between the patients′ exclusive time and the qualified rate of bacterial culture of endoscopes(r=0.860,P=0.028).CONCLUSION The patients′ exclusive time is closely related to the quality of endoscopic disinfection,which can be an important reference to forecast the qualified rate of the endoscopes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".