A meta-analysis of the correlation between carbapenem antibiotic use and the incidence of carbapenem-resistant Pseudomonas aeruginosa
Bibliographic record
Abstract
INTRODUCTION: This meta-analysis evaluates the correlation between carbapenem antibiotic use and the incidence of carbapenem-resistant Pseudomonas aeruginosa (CRPA). METHODOLOGY: A comprehensive literature search conducted across multiple databases yielded seven clinical experimental studies involving 4,417 patients. The primary outcomes assessed were the risk factors associated with CRPA infection, drug resistance rates, and the comparison of resistance rates between meropenem (MEM) and imipenem (IPM). The Newcastle-Ottawa Scale (NOS) was used to assess study quality, and Egger's test and funnel plots were used to assess publication bias. RESULTS: The NOS scores for the included studies ranged between 6 and 8, indicating their generally high quality. The analysis indicated that prior carbapenem use significantly increased the risk of CRPA infection (OR = 1.866, 95% confidence interval [CI]: 1.164-2.993, p = 0.010). The drug resistance rates of P. aeruginosa to carbapenems ranged between 21.07% and 37.90%. There was no significant difference in drug resistance rates between MEM and IPM (risk ratio = 1.09, 95% CI: 0.99-1.21, p = 0.517). CONCLUSIONS: With drug resistance rates between 21.07% and 37.90%, these findings suggest that carbapenem use is associated with an increased risk of CRPA infection, highlighting the need for the judicious use of these antibiotics in clinical practice.
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 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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.062 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".