Lebanese Registry in the Management of Antibiotic Associated Diarrhea in Children: Observational Study in Daily Practice
Bibliographic record
Abstract
Background: Antibiotic-associated diarrhea (AAD) is a common complication in patients prescribed antibiotics, and represents an economic and health burden. Evidence that probiotics may be beneficial for the prevention of AAD is increasing. The aims of this registry were to assess the prevalence of probiotic prescriptions in pediatric patients for whom an antibiotic treatment regimen was prescribed, and to explore the potential health benefits that such an administration may provide. Methods: This longitudinal, multicenter, observational study enrolled 249 pediatric patients prescribed an antibiotic treatment for 5 - 14 days, with or without a concomitant probiotic. The number of probiotic-administered patients, and AAD incidence rates throughout the 15-day follow-up period, were assessed. Results: Of the 246 patients who met inclusion/exclusion criteria in Lebanon, the investigators had prescribed an additional probiotic treatment to 118 (48%) of them, while, the other 128 (52%) did not receive such additional treatment. A significantly higher number of patients in the probiotic group were at high risk of developing diarrhea (probiotic: 27.1% vs. no probiotic: 6.3%; P < 0.001). Among high risk patients, the frequency of diarrhea was doubled in the group with no probiotics (probiotic: 21.9% (n = 7) vs. no probiotic: 50.0% (n = 4); P = 0.182). Despite the significantly larger number of probiotic-administered patients that were at high risk of developing diarrhea, the proportion of patients who reported developing diarrhea was not statistically different between the two groups (probiotic: n = 22 (18.6%) vs. no probiotic: n = 24 (18.8%); P = 0.983). Conclusions: In conclusion, this Lebanese disease registry demonstrated that almost half of pediatric patients with mild to moderate infections were prescribed probiotics in combination with antibiotics to decrease the risk of AAD. This observation was particularly significant in the high risk population as per the treating physicia n’ s judgment. Int J Clin Pediatr. 2017;6(1-2):8-19 doi: https://doi.org/10.14740/ijcp269w
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How this classification was reachedexpand
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".