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Effect of a Comprehensive Antibiotic Stewardship Programme on Antibiotic Prescribing for Acute Respiratory Infections in China's Rural Primary Care Facilities: A Cluster-randomised Controlled Trial

2025· article· en· W4410271439 on OpenAlexaff
Xiaolin Wei, Chao Zhuo, Joseph Paul Hicks, Zengqiang Zhang, Shouling Wu, John Walley, Jinping Zheng, Weihua Guo, Hui Huang, Frank Sullivan, A. Brown, Nanshan Zhong

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineAntibiotic StewardshipAntibioticsStewardship (theology)ChinaIntensive care medicineAntimicrobial stewardshipPrimary careCluster (spacecraft)Family medicineAntibiotic resistance

Abstract

fetched live from OpenAlex

Abstract Rationale: Antibiotic stewardship interventions may reduce antibiotic prescribing. The study aims to assess the effects of comprehensive antibiotic stewardship in reducing antibiotic prescribing for acute respiratory infections. Methods: We conducted a pragmatic, cluster randomized controlled trial conducted in 34 primary care facilities in rural Guangdong Province, China. Clusters were primary care facilities in two counties with a total of 1 million population. We randomized facilities to intervention or control groups in an overall 1:1 ratio stratified by county. We collected prescription data from all outpatients aged 0-75 who visited the primary care facilities during the 12-month baseline and 12-month intervention periods if they received a primary diagnosis of any acute respiratory infection except pneumonia. Interventions: Our intervention included a half-day training session for doctors using evidence-based guidelines on acute respiratory disease, regular prescription review and feedback facilitated by an app linked with electronic medical records. Patients received app-based education. Control facilities received no inputs. Main outcome and measures: The primary outcome was whether a patient was prescribed antibiotics during the 12-month trial period. We measured the percentage of patients hospitalized within 30 days for respiratory illness or sepsis as a safety indicator of withholding antibiotics. Results: We recruited 34 township hospitals (17 per county) and randomized 17 to each treatment group. Between the baseline (1 January 1 2019 to 15 December 2019) and the intervention period (1 March 2020 and 28 February 2021), the antibiotic prescribing rate decreased from 83% (88828/107314) to 26% (14521/54799) in the intervention group and from 84% (61486/72796) to 71% (30340/42440) in the control group. After adjustment for stratum and covariates, the risk difference was -39 percentage points (95% CI: -47, -29; p<0.001). The intervention did not affect the 30-day hospitalization rate with adjusted risk difference of 0·43 percentage points (95% CI: -0·02, 0·8; p = 0·058). Conclusions: In primary care facilities, antibiotic stewardship involving training guidelines, prescription review and feedback resulted in a substantial reduction in antibiotic prescribing rates for acute respiratory infections without safety concerns. Our trial will inform the antibiotic stewardship policy for primary care facilities in China. Trial registration: ISRCTN, ISRCTN96892547

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.011
GPT teacher head0.303
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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