The British Society for Antimicrobial Chemotherapy Resistance Surveillance Project: methods and limitations
Bibliographic record
Abstract
OBJECTIVES: The BSAC Bacteraemia and Respiratory Resistance Surveillance Programmes provided long-term surveillance of antibiotic resistance in key pathogens of bloodstream and both community- and hospital-acquired respiratory infections in the UK and Ireland. This paper details the methodologies used. Data limitations are discussed. METHODS: Sentinel laboratories across the UK and Ireland contributed up to a fixed annual quota of isolates of defined bacterial groups. For each Programme, a Central Laboratory confirmed bacterial identifications, measured MICs by the BSAC agar dilution method, investigated mechanisms of resistance and determined serotypes of Streptococcus pneumoniae. Identification methods evolved over time, e.g. with adoption of MALDI-TOF. Classification of susceptibility and resistance follows the 2022 (not contemporaneous) EUCAST guidance. RESULTS: Seventy-nine laboratories contributed 30 716 community respiratory isolates from 1999/2000 to 2018/19; 65 laboratories contributed 13 508 hospital respiratory isolates from 2008/09 to 2018/19; 81 laboratories contributed 56 064 bacteraemia isolates from 2001 to 2019. Although large and teaching hospitals were over-represented, the resistance rates for bacteraemia organisms collected in England mirror more extensive (but less standardized or detailed) national data gathered from laboratories by the UK Health Security Agency and its predecessor organizations, which provided a bespoke data extract. CONCLUSIONS: These surveillance Programmes have provided comprehensive and reliable information on antibiotic susceptibility in the UK and Ireland over two decades. Detailed results, showing resistance trends and mechanisms of antibiotic resistance, are presented in five papers in this Supplement.
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 | 0.123 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".