Gastrointestinal dysbiosis and the use of fecal microbial transplantation in<i>Clostridium difficile</i>infection
Bibliographic record
Abstract
The impact of antibiotics on the human gut microbiota is a significant concern. Antibiotic-associated diarrhea has been on the rise for the past few decades with the increasing usage of antibiotics. Clostridium difficile infections (CDI) have become one of the most prominent types of infectious diarrheal disease, with dramatically increased incidence in both the hospital and community setting worldwide. Studies show that variability in the innate host response may in part impact upon CDI severity in patients. That being said, CDI is a disease that shows the most prominent links to alterations to the gut microbiota, in both cause and treatment. With recurrence rates still relatively high, it is important to explore alternative therapies to CDI. Fecal microbiota transplantation (FMT) and other types of bacteriotherapy have become exciting avenues of treatment for CDI. Recent clinical trials have generated excitement for the use of FMT as a therapeutic option for CDI; however, the exact components of the human gut microbiota needed for protection against CDI have remained elusive. Additional investigations on the effects of antibiotics on the human gut microbiota and subsequent CDI will help reduce the socioeconomic burden of CDI and potentially lead to new therapeutic modalities.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".