Investigation Concerning Combined Use of Acidophilus Biologics and Antibiotics or Anticancer Drugs
Bibliographic record
Abstract
Diarrhea is a very common,important adverse effect associated with antibiotic therapy.For the prevention and treatment of diarrhea,Biofermin (BF) probiotic preparations are prescribed frequently and an antibiotic-resistant probiotic BioferminR (BFR) is particularly useful against antibiotic-associated diarrhea.BFR is often prescribed in combination with antibiotics to which the probiotic is susceptible or with off-label drugs such as anti-cancer drugs.We investigated the concurrent usage of such probiotics and antibiotics and anti-cancer drugs using data from the database of a computer order entry system at the Tokushima University Hospital (April 2006 to March 2008),as well through the use of a questionnaire given to hospital doctors.The results of our study suggested that not all doctors had a sufficient knowledge of the usage of BFR when prescribing it.In routine pharmacy work,pharmacists should therefore actively question doctors concerning prescriptions they have written for the combination of BFR and off-label drugs to ensure the proper use of BFR and prevent the emergence of drug-resistant bacteria due to bacterial mutagenicity associated with antibiotics and anti-cancer drugs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".