Measures used to assess the burden of ESBL-producing <i>Escherichia coli</i> infections in humans: a scoping review
Bibliographic record
Abstract
BACKGROUND: (ESBL-EC) in the published literature, indicating a need to synthesize available BOI data to provide an overall understanding of the impact of ESBL-EC infections on human health. OBJECTIVES: To summarize the characteristics of BOI reporting in the ESBL-EC literature to (i) describe how BOI associated with antimicrobial resistance (AMR) is measured and reported; (ii) summarize differences in other aspects of reporting between studies; and (iii) highlight the common themes in research objectives and their relation to ESBL-EC BOI. METHODS AND RESULTS: Two literature searches, run in 2013 and 2018, were conducted to capture published studies evaluating the BOI associated with ESBL-EC infections in humans. These searches identified 1723 potentially relevant titles and abstracts. After relevance screening of titles and abstracts and review of full texts, 27 studies were included for qualitative data synthesis. This review identified variability in the reporting and use of BOI measures, study characteristics, definitions and laboratory methods for identifying ESBL-EC infections. CONCLUSIONS: Decision makers often require BOI data to make science-based decisions for the implementation of surveillance activities or risk reduction policies. Similarly, AMR BOI measures are important components of risk analyses and economic evaluations of AMR. This review highlights many limitations to current ESBL-EC BOI reporting, which, if improved upon, will ensure data accessibility and usefulness for ESBL-EC BOI researchers, decision makers and clinicians.
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 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 | 0.053 | 0.177 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.033 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".