An analysis of antibiotic prescribing practices for enteric bacterial infections within FoodNet Canada sentinel sites
Bibliographic record
Abstract
OBJECTIVES: Understanding the current state of antibiotic treatment guidelines and prescribing practices for bacterial enteric infections is critical to inform antibiotic stewardship initiatives. This study aims to add to the current understanding through three objectives: (i) to identify and summarize published treatment guidelines for bacterial enteric infections; (ii) to describe observed antibiotic prescribing practices for bacterial enteric infections across three sentinel sites in Canada; and (iii) to assess concordance between observed antibiotic prescribing and treatment guidelines. METHODS: An environmental scan of treatment guidelines for bacterial enteric infections was conducted and recommendations were collated. A descriptive analysis of cases of bacterial enteric illnesses captured in FoodNet Canada's sentinel site surveillance system between 2010 and 2018 was performed. Antibiotic-use data were self-reported by cases via an enhanced questionnaire. RESULTS: Ten treatment guidelines were identified in the environmental scan. There was substantial variation between guidelines for both when to prescribe antibiotics and which antibiotics were recommended. Of the 5877 cases of laboratory-confirmed bacterial enteric illness in the three sites, 49% of cases reported having received an antibiotic prescription. Of particular significance was the finding that 21% of verotoxigenic Escherichia coli cases received a prescription. Of the 17 antibiotics recommended in the guidelines, 14 were used in practice. In addition to these, 18 other antibiotics not included in any of the guidelines reviewed were also prescribed. CONCLUSIONS: Our study suggests that a substantial proportion of enteric bacterial infections in Canada are prescribed antibiotics. These findings highlight the need to standardize treatment guidelines for enteric illnesses and could be used to inform future stewardship programme development.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".