Bibliographic record
Abstract
Sir—Beaulieu et al. [1] and Weiss [2] assert that the key elements of control of Clostridium difficile—associated diarrhea (CDAD) are isolation of patients and cleaning, rather than antibiotic stewardship, but they provide few data supporting their view. Few people would oppose better infection control in any hospital for the same reason that few people oppose sunny weather: it is obviously a good idea. However, the relevant questions for clinicians who are confronted with this emerging strain of C. difficile and want to reduce its devastating morbidity and mortality rates are as follows: can we rely on the business-as-usual recipe of strengthening infection control measures, and if not, what else should we do? Although we agree with Weiss [2] that infection-control procedures in Quebec hospitals were suboptimal prior to this epidemic, the experience of many hospitals, including ours, was that the improvement of infection-control measures during August—September 2003 had, unfortunately, no impact whatsoever on the incidence of CDAD, which became truly catastrophic during the winter of 2003–2004 [3]. This disappointing impact of traditional infection-control measures against C. difficile, known for years, is thought to be a consequence of the prolonged survival of its spores in the hospital environment and on the hands of hospital personnel. In 1995, the Society for Healthcare Epidemiology of America stated that “the most successful control measure directed at reduction of symptomatic disease has been antimicrobial restriction” [4, p. 459].
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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.038 | 0.062 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".