Bibliographic record
Abstract
Antimicrobial resistance is a growing threat to the effective treatment of an ever-increasing range of infections caused by microorganisms, especially bacteria and fungi. The rise of resistance has clear implications for the choice of empirical therapy and may influence treatment outcomes. Detailed knowledge of the rates of antimicrobial resistance at both local and national levels is essential to inform such choices. Therefore, it is very welcome that in this JAC Supplement, data from the CANWARD study are presented. The CANWARD surveillance programme monitors rates of resistance in a range of bacterial and fungal pathogens causing infections in both inpatients and outpatients in Canada. In this Supplement, 10 years of longitudinal surveillance data (2007–16) are presented. Pathogens included are carbapenem-resistant and XDR Pseudomonas aeruginosa, ESBL-producing Escherichia coli and Klebsiella pneumoniae, Streptococcus pneumoniae, MRSA and Candida species. National surveillance data at this scale are both rare and invaluable when determining or contrasting antimicrobial resistance trends over time in specific geographic areas. In addition, the international nature of the antimicrobial resistance epidemiology means that these data should be of interest to public health and clinical researchers across the globe. Engeline van Duijkeren Russell Hope Nathan P. Wiederhold None to declare.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.527 | 0.340 |
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".