Bibliographic record
Abstract
Antibiotic treatment may destroy beneficial intestinal flora, often resulting in diarrhea. Between 3% and 22% of patients in hospital have antibioticrelated diarrhea, and this may cause pseudomembranous colitis. 1 Several placebo-controlled studies suggest that taking probiotics may reduce the incidence of diarrhea in patients receiving antibiotic treatment. 2–5 Probiotics are live microbial supplements such as Lactobacillus acidophilus that beneficially affect humans by altering their intestinal microbial balance. To determine whether physicians recommend probiotics to their patients to prevent antibiotic-related diarrhea, I surveyed a sample of family physicians in Nova Scotia. I mailed a cover letter, brief questionnaire on probiotic use and a stamped return envelope to the first 100 family physicians, in alphabetical order, listed in the telephone directories for northeastern and central Nova Scotia. Of the 100 surveys sent out, 66 were completed and returned. Three physicians returned the questionnaire uncompleted, stating that they were no longer in active practice. This resulted in a survey response rate of 68%. The responding physicians were in practice for as few as 2 years to as many as 57 years (mean 19.7 years). All of the respondents stated that they prescribe antibiotics on a regular basis. Only 21 (32%) reported that they recommend probiotics to their patients when prescribing antibiotics; of these, 10 stated that they do so always or often, and 11 seldom do so. Only 12 physicians (18%) indicated that they were aware of any research on probiotics. Fortytwo respondents provided reasons for not recommending the use of probiotics: 13 (31%) stated that there was not enough research to support such use, 13 (31%) were not familiar with probiotics, 10 (24%) felt that they were not necessary, 2 (5%) stated that it was not an accepted practice to recommend their use, and 4 (10%) gave other, miscellaneous reasons. Most of the family physicians who participated in this survey are not recommending probiotic use to their patients when prescribing antibiotics. However, physicians appear to be open to the possibilities of probiotic use. Some respondents commented that they are aware of anecdotal evidence that probiotics are effective in treating antibiotic-induced illnesses. The majority of respondents felt that more research is required into the effect of combining antibiotic treatment with probiotics (88%) and that more information about probiotics is needed (82%). Physicians need to be made aware of the current information available on probiotics, and more high-quality, double-blind trials are needed to answer some of the questions raised by physicians.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".