When It Comes to Stewardship, It’S Time to Get with the Programmers
Bibliographic record
Abstract
Reducing antimicrobial use is believed to be a critical intervention in an era of impending catastrophic drug resistance, with little promise in the antimicrobial pipeline (1,2). Up to one-half of human antimicrobial use is believed to be inappropriate in terms of indication, choice of agent or duration (3). After years of research, it is clear that the most important determinant of resistance development is the use of an antimicrobial (4,5). In an effort to counteract overuse, Accreditation Canada now mandates, in its Required Organizational Practices, the existence of a multidisciplinary antimicrobial steward-ship program (ASP) at most inpatient health care facilities, including long-term care facilities providing ‘complex continuing care’ (6). Successful ASPs have demonstrated benefits including reduced drug resistance, fewer Clostridium difficile infections and reduced anti-microbial-related toxicity, with no demonstrated adverse clinical outcomes .
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 | 0.023 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.014 | 0.036 |
| Insufficient payload (model declined to judge) | 0.048 | 0.018 |
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".