Non-antibiotic strategies for the prevention/treatment of<i>Clostridium difficile</i>infection
Bibliographic record
Abstract
Background: Clostridium difficile infection has become a serious concern in both hospital and secondary healthcare environments. In the presence of repeated or prolonged antibiotic treatment, the C. difficile spores can germinate in the colon and produce toxins that cause colonic inflammation and diarrhea. The standard treatment for C. difficile-associated disease (CDAD) usually involves the withdrawal of the antibiotic treatment that led to the CDAD followed by a course of oral metronidazole or vancomycin, but there has been an increasing number of treatment failures and recurrences of disease. Over the past 10 – 15 years, researchers have begun exploring the possibility of using alternative means to combat C. difficile infection. Objective/methods: Over the course of the past 5 years, there has been a considerable amount of patent literature focused on non-antibiotic alternatives, including passive and active immunizations, monoclonal antibodies, antitoxins, inert binders and probiotic therapies. Results/conclusion: Current antibiotic therapies for the treatment of CDAD are not as effective as they once were. There is some promising work on non-antibiotic alternatives for CDAD prevention and treatment.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".