Comparison of target monitoring of nosocomial infection in intensive care units among different classes of hospitals in Jiangsu province
Bibliographic record
Abstract
Objective To explore the effect of target monitoring of nosocomial infection in intensive care units （ ICU） among different classes of hospitals during different phases of time .Methods According to the“nosocomial infection monitoring standards” （ WS/T312-2009 ） promulgated by the Ministry of Health in 2001 , every hospital participated the ICU nosocomial infection target monitoring .Each season , the data about nosocomial infection quality control was reported .Then, the nosocomial infection quality control center checked the data, and published the statistical results , and completed a quality analysis .The monitoring data of the first quarter of 2012 and 2013 were analyzed by using Stata10.0.Results The infection rate in ICU per day, adjusted day infection rate , cases day infection rate in the first quarter of 2013 were 10.45‰, 2.91‰, 13.58‰, which were significantly lower than those of 12.19‰, 3.23‰, 17.44‰ in the first quarter of 2012 （χ2 =9.869, 8.161, 64.941, respectively; P <0.01 ）.The primary site of nosocomial infection was respiratory system .The day infection rate of ventilator associated pneumonia （ VAP） in the first quarter of 2013 was lower than that in the first quarter of 2012 （P<0.05）.Conclusions Patients in ICU are susceptible to the nosocomial infection .Targeted monitoring is a useful measure to discover the risk factors of nosocomial infection . It can help us take specific measures , and effectively reduce the incidence of nosocomial infection among ICU patients. Key words: Intensive care units; Nosocomial infection; Target monitoring
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".