A probiotic drink prevented diarrhoea and<i>Clostridium difficile–</i>associated diarrhoea in older patients taking antibioticsCommentary
Bibliographic record
Abstract
M Hickson Dr M Hickson, Charing Cross Hospital, London, UK; mary.hickson@imperial.nhs.uk Does a probiotic drink prevent diarrhoea and Clostridium difficile– associated diarrhoea in older adults receiving antibiotics in hospital? ### Design: randomised controlled trial (RCT). ### Allocation: concealed. ### Blinding: blinded (patients, {clinicians, data collectors, outcome assessors, data analysts, monitoring committee}*). ### Follow-up period: 4 weeks after discharge. ### Setting: 3 hospitals in London, UK. ### Patients: 135 patients >50 years of age (mean age 74 y, 54% women) who were receiving antibiotics (single or multiple, oral or intravenous) and were able to take food and drinks. Exclusion criteria included diarrhoea on admission or recurrent diarrhoea; bowel pathology that could cause diarrhoea; intake of high-risk antibiotics (ie, clindamycin, cephalosporins, or aminopenicillins) or >2 courses of other antibiotics in the past 4 weeks; severe life-threatening illness; immunosuppression; bowel surgery; artificial heart valve; history of rheumatic heart disease or infective endocarditis; probiotic treatment before admission; and intolerance to lactose …
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".