Exploration de l’association possible entre la consommation d’antibiotiques et l’émergence de résistance dans un centre hospitalier universitaire mère-enfant
Bibliographic record
Abstract
Contexte : L’émergence de résistances aux antibiotiques a contribué à créer des bactéries multirésistantes, et ceci constitue une préoccupation majeure. Objectifs : L’objectif principal était d’explorer l’association possible entre la consommation d’antibiotiques et l’émergence de résistance dans un centre hospitalier universitaire mère-enfant. Méthode : Il s’agit d’une étude rétrospective menée dans un centre hospitalier universitaire. Des couples antibiotique-bactérie ont été établis, en tenant compte du nombre d’isolats, de l’utilisation réelle d’antibiotique et de la pertinence clinique des couples. Pour chaque couple, une comparaison de deux variables (consommation d’antibiotiques et pourcentage de résistance) a été quantifiée par un coefficient de Pearson. Trois analyses ont été considérées : sans décalage entre les données d’utilisation et de résistance, décalage d’un an et décalage de deux ans. Résultats : Trente couples ont été sélectionnés en hémato-oncologie et 18 couples en néonatologie. En hémato-oncologie, six couples avaient une corrélation positive (Pearson > 0,7), deux couples impliquant le méropénem, deux la ceftazidime et deux la pipéracilline-tazobactam. Dans trois cas, la corrélation s’observe sans décalage entre les données de consommation et de résistance. En néonatologie, trois couples avaient une corrélation positive, un impliquant la vancomycine, un la cloxacilline et un le méropénem. Conclusions : Il est possible d’explorer l’association qui peut exister entre la consommation d’antibiotiques et l’émergence de résistance dans un centre. L’approche exploratoire reposait sur un traitement manuel des données. Il pourrait être intéressant d’envisager une approche systématique continue permettant la production automatique de corrélations. ABSTRACT Background: The emergence of antibiotic resistance has contributed to the development of multidrug-resistant bacteria, which is a major concern. Objectives: The primary objective was to explore the possible association between antibiotic use and the emergence of resistance in a mother-child university hospital. Method: This retrospective study was conducted in a university hospital centre. Antibiotic-bacteria pairs were established, taking into account the number of isolates, actual antibiotic use, and clinical relevance. For each pair, a comparison of 2 variables (antibiotic utilization and rate of resistance) was quantified with the Pearson coefficient. Three analyses were conducted: no lag between utilization and resistance, 1-year lag, and 2-year lag. Results: Thirty antibiotic-bacteria pairs were selected from hematology-oncology and 18 from neonatology. In hematology-oncology, 6 pairs had a positive correlation (Pearson coefficient > 0.7): 2 pairs involving meropenem, 2 involving ceftazidime, and 2 involving piperacillin-tazobactam. In 3 of these cases, there was no lag between consumption of antibiotics and presence of resistance. In neonatology, 3 antibiotic-bacteria pairs had a positive correlation, 1 each involving vancomycin, cloxacillin, and meropenem. Conclusions: It is possible to explore the potential association between consumption of antibiotics and emergence of resistance in a particular centre. Our exploratory approach was based on manual data processing. It would be interesting to consider a continuous systematic approach, allowing automatic generation of correlations.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".