An insight into the health-promoting effects of foods to prevent Antibiotic- associated Diarrhoea: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The random use of antibiotics leads to gastrointestinal diseases with complications ranging from mild diarrhea to Pseudomembranous colitis, which is called antibiotic-associated diarrhea. Outbreaks of antibiotic-associated diarrhea are generally found in 10% to 30% of patients taking antibiotics, depending on the particular antibiotic used, and are caused by Clostridium difficile in general and Klebsiella oxytoca, Staphylococcus aureus, Clostridium perfringens, etc. in particular. Functional disturbances of intestinal carbohydrates, harmful effects of antibiotics on the intestinal mucosa, and allergic effects are responsible for mild antibiotic-associated diarrhea. In COVID-19, caused by SARS-CoV-2, patients treated with a variety of antibiotics have been noticed to suffer from severe antibiotic-associated diarrhea. Probiotics have been shown to play a significant role in preventing antibiotic- associated diarrhea in both COVID-19 patients and general patients. Prebiotics and dietary approaches also play a vital role in combating antibiotic-associated diarrhea. Given the increased demand for food associated with the treatment of antibiotic-associated diarrhoea, the current review paper attempted to orchestrate the effect of probiotics such as yogurt, kefir, cheese, probiotic milk, and dietary foods such as ripe papaya, bananas, and other fermented foods in developing an immune system capable of effectively combating antibiotic-associated diarrhea.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it