Measures used to assess the burden of ESBL-producing <i>Escherichia coli</i> infections in humans: a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it