Distribution and drug resistance of Acinetobacter baumannii infection in patients with lung diseases
Bibliographic record
Abstract
OBJECTIVE To explore the distribution and drug susceptibility of Acinetobacter baumaanii in patients with lung diseases,so as to prevent the correlated infections.METHODS Routine bacterial identification for the submitted specimens in respiratory department from 2009 to 2010 was performed with ATB,the specimens infected with A.baumaanii were screened out,statistical analysis of A.baumaanii infections and the distribution of diseases was performed;the drug susceptibility testing for the isolates was carried out according to CLSI standards,the drug resistance was analyzed.RESULTS Totally 165 strains of A.baumaanii were isolated from 1020 specimens with the detection rate of 16.2%;the specimens mainly obtained from sputum,blood,secretions,urine and others,accounting for 57.6%,21.8%,11.5%,4.9%,and 4.2%,respectively,as compared with the sputum specimens,there were statistical significance(P0.01);A.baumaanii commonly distributed in the patients with ventilator-associated pneumonia,lung cancer,or tuberculosis,the difference between VAP and other diseases was statistically significant(P0.05);the result of drug susceptibility testing showed that A.baumaanii was susceptible to imipenem,cefoperazone/sulbactam,piperacillin/tazobactam,ofloxacin,cefepime,aztreonam,and piperacillin,and was resistant to the rest of antibiotics;the susceptibility rates to imipenem and cefoperazone/sulbactam were 52.7% and 38.8%,respectively,the difference in susceptibility rate was statistically significant as compared with other antibiotics(P0.05).CONCLUSION A.baumaanii isolates are with the highest detection rate from sputum specimens and are most common in the patients with VAP,the drug resistance rate is high,A.baumaanii isolates are relatively susceptible to imipenem and cefoperazone/sulbactam,according to drug susceptibility testing to enhance the therapeutic effect,so it is necessary to take a comprehensive consideration to improve the therapeutic effect on the basis of drug susceptibility testing.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".