A Consensus Development Conference Model for Establishing Health Policy for Surveillance and Screening of Antimicrobial-Resistant Organisms
Bibliographic record
Abstract
The Canadian Consensus Development Conference on Surveillance and Screening for Antimicrobial-Resistant Organisms (AROs) was sponsored by the Alberta Ministry of Health to provide evidence to update policies for ARO screening in acute care settings. A rigorous evidence-based literature review completed before the conference concluded that that neither universal nor targeted screening of patients was associated with a reduction in hospital-acquired ARO colonization, infection, morbidity, or mortality. Leading international clinicians, scientists, academics, policy makers, and administrators presented current evidence and clinical experience, focusing on whether and how hospitals should screen patients for AROs as part of broader ARO control strategies. An unbiased and independent "jury" with a broad base of expertise from complementary disciplines considered the evidence and released a consensus statement of 22 recommendations. Policy highlights included developing an integrated "One Health" strategy, fully resourcing basic infection control practices, not performing universal screening, and focusing original research to determine what works.
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 | 0.303 | 0.242 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.013 | 0.019 |
| Research integrity | 0.023 | 0.029 |
| Insufficient payload (model declined to judge) | 0.011 | 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".