Risk factors associated with antibiotic prescriptions for cases of enteric pathogens in Canada, 2015–2019
Bibliographic record
Abstract
Inappropriate antibiotic use is a key driver of antibiotic resistance and one that can be mitigated through stewardship. A better understanding of current prescribing practices is needed to develop successful stewardship efforts. This study aims to identify factors that are associated with human cases of enteric illness receiving an antibiotic prescription. Cases of laboratory-confirmed enteric illness reported to the FoodNet Canada surveillance system between 2015 and 2019 were the subjects of this study. Laboratory data were combined with self-reported data collected from an enhanced case questionnaire that included demographic data, illness duration and symptoms, and antibiotic prescribing. The data were used to build univariable logistic regression models and a multivariable logistic regression model to explore what factors were associated with a case receiving an antibiotic prescription. The final multivariable model identified several factors as being significantly associated with cases being prescribed an antibiotic. Some of the identified associations indicate that current antibiotic prescribing practices include a substantial level of inappropriate use. This study provides evidence that antibiotic stewardship initiatives targeting infectious diarrhoea are needed to optimize antibiotic use and combat the rise of antibiotic resistance.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".