Characterisation of burden of illness measures associated with human (Fluoro)quinolone-resistant <i>Campylobacter</i> spp. infections – a scoping review
Bibliographic record
Abstract
Abstract Campylobacter spp. are one of the most common causes of bacterial gastroenteritis in Canada and worldwide. Fluoroquinolones are often used to treat complicated human campylobacteriosis and strains of Campylobacter spp. resistant to these drugs are emerging along the food chain. A scoping review was conducted to summarise how human (fluoro)quinolone-resistant (FQR; quinolones including fluoroquinolones) Campylobacter spp. infections are characterised in the literature by describing how burden of illness (BOI) associated with FQR is measured and reported, describing the variability in reporting of study characteristics, and providing a narrative review of literature that compare BOI measures of FQR Campylobacter spp. infections to those with susceptible infections. The review identified 26 studies that yielded many case reports, a lack of recent literature and a lack of Canadian data. Studies reported 26 different BOI measures and the most common were hospitalisation, diarrhoea, fever and duration of illness. There were mixed results as BOI measures reported in literature were inconsistently defined and there were limited comparisons between resistant and susceptible infections. This presents a challenge when attempting to assess the magnitude of the BOI due to FQR Campylobacter spp., highlighting the need for more research in this area.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".