Local antibiotic resistance surveillance in nursing homes using lot quality assurance sampling: a feasibility study
Bibliographic record
Abstract
OBJECTIVES: To assess the feasibility of lot quality assurance sampling (LQAS) for defining high prevalence of antibiotic-resistant (ABR) uropathogens in Dutch nursing homes. METHODS: In this cross-sectional study, we constructed 'lots' of residents with urinary tract infections (UTIs) across three Dutch nursing home organizations (21 locations, 2095 beds) between February and July 2023. Resistance thresholds for common antibiotics used in empirical antibiotic therapy (EAT) were set at 20% (upper) and 5% (lower). Per organization ≥44 strains of E. coli/Klebsiella spp. were consecutively sampled; ≥ 5 resistant strains indicated 'high' ABR prevalence. Aggregate data were used in an adapted, meta-regression model to identify structural ABR determinants. Three multidisciplinary focus groups were conducted to identify implementation requirements for LQAS-based ABR surveillance. RESULTS: From 298 urine specimens 132 E. coli/Klebsiella strains were identified. Across all three organizations, amoxicillin-clavulanic acid (first-choice EAT for pyelonephritis) was classified as 'high', while seven other antibiotics, including three EAT agents, had varying classifications. Higher ABR was associated with higher proportions of rehabilitation/short-stay care beds, higher number of shared bathrooms, higher hospital admission rates and higher antibiotic usage. Focus groups revealed that defining a ABR upper threshold for LQAS was difficult, that multiple factors influenced the choice of EAT (e.g. side effects), and that the role of ABR prevalence in EAT-related decision making was unclear. CONCLUSIONS: Using LQAS to classify E. coli/Klebsiella ABR in nursing homes was feasible within 4 months. To effectively inform local EAT, consensus is needed on the threshold of maximum acceptable level of ABR.
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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".