Antimicrobial‐Associated Risk Factors for<i>Clostridium difficile</i>Infection
Bibliographic record
Abstract
Antimicrobial therapy plays a central role in the pathogenesis of Clostridium difficile infection (CDI), presumably through disruption of indigenous intestinal microflora, thereby allowing C. difficile to grow and produce toxin. Investigations involving animal models and studies performed in vitro suggest that inhibitory activity against C. difficile and differences in the propensity to stimulate toxin production may also influence the likelihood that particular drugs may cause CDI. Although nearly all antimicrobial classes have been associated with CDI, clindamycin, third-generation cephalosporins, and penicillins have traditionally been considered to harbor the greatest risk. Recent studies have also implicated fluoroquinolones as high-risk agents, a finding that is most likely to be related in part to increasing fluoroquinolone resistance among epidemic strains (i.e., restriction-endonuclease analysis group BI/North American PFGE type 1 strains) and some nonepidemic strains of C. difficile. Restrictions in the use of clindamycin and third-generation cephalosporins have been associated with reductions in CDI. Because use of any antimicrobial has the potential to induce the onset of CDI and disease caused by other health care-associated pathogens, antimicrobial stewardship programs that promote judicious use of antimicrobials are encouraged in concert with environmental and infection control-related efforts.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".