Monitoring of blood culture submission of 72 hospitals in 2011 Shanghai
Bibliographic record
Abstract
OBJECTIVE To investigate the submission rate of the blood culture of 72 hospitals in 2011.METHODS The data of the blood culture submission were investigated for the hospitalized patients with fever(≥38.5 ℃) from 72 hospitals under the surveillance net by Shanghai Center for Nosocomial Infection Control for 3 days once and once a quarter.RESULTS The patients enrolled from 72 hospitals in the first,second,third and fourth quarter were 54968,51089,54089 and 49421.The number of patients with fever(≥38.5 ℃) were 1602,1347,1571,and 1443,respectively,among them,the proportions of blood culture submission were 604(37.70%),572(42.78%),937(59.64%) and 640(44.35%),respectively,the submission rate was increasing;the number of patients with fever(≥39.5 ℃) was 1196 with the submission rate of 59.20%;the number of patients with CVC(≥5 days) was 937 with the submission rate of 52.50%;the submission rate of the patients who used special antibiotics was 47.25%.CONCLUSION The submission rate of the blood culture of the 72 hospitals in Shanghai needs to be further improved,it is necessary to take the steps to raise the submission rate of the blood culture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".