MétaCan
Menu
Back to cohort
Record W4414938179 · doi:10.3855/jidc.20950

A meta-analysis of the correlation between carbapenem antibiotic use and the incidence of carbapenem-resistant Pseudomonas aeruginosa

2025· review· en· W4414938179 on OpenAlexaboutno aff
Cheng Yee Tang, Hong Fang, Min Lv

Bibliographic record

VenueThe Journal of Infection in Developing Countries · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsnot available
Fundersnot available
KeywordsIncidence (geometry)Pseudomonas aeruginosaAntibioticsCarbapenemAntibiotic resistanceDrug resistanceAntibacterial agent

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.208
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.311
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Infection in Developing CountriesSame topicAntibiotic Resistance in BacteriaFrench-language works237,207