Combined application of management tools in improvement of effectiveness of infection control
Bibliographic record
Abstract
OBJECTIVE To explore the effect of combined application of management tools on the control of nosocomial infection so as to recue the incidence of central catheter-associated bloodstream infection in the ICU.METHODS The defects of infections control that were displayed in the conventional data surveillance for 74 catheterization patients of neurology department ICU and general surgery department ICU in the first quarter of 2014 were chosen as the intervention objects.The combined application of the management tools such as root cause analysis,cause and effect diagram,and PDCA was carried out,the causes and root causes of the existing problems were analyzed,the rectification measures were developed in response to the root causes,the rectification effect was dynamically evaluated by referring to the Real-time nosocomial infection surveillance system so as to sum up the experience and make the related system and procedure prefect.RESULTS After the combined application of management tools for intervention,the incidence of central catheter-bloodstream infections in the neurology department ICU dropped from 3.24cases/1000 catheter day in the first quarter of 2014 to 0case/1000 catheter day in the first quarter of 2015;the incidence of central catheter-bloodstream infections decreased from 5.51cases/1000 catheter day in the first quarter of 2014 to 1.68cases/1000 catheter day in the first quarter of 2015.CONCLUSION The combined application of management tools in the control of nosocomial infection may contribute to the discovery of hidden dangers of the system,raise the accuracy and efficiency of the rectification,and intensify the rectification effect and management efficiency.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".