Efeito do Lactobacillus reuteri na cólica infantil: revisão baseada na evidência
Bibliographic record
Abstract
Objective: To review the evidence for the effectiveness of probiotic supplementation with Lactobacillus reuteri in reducing symptoms of infantile colic. Data sources: National Guideline Clearinghouse, Guideline Finder, Canadian Medical Association, The Cochrane Database, DARE, Bandolier and MEDLINE/PubMed. Methods: A review of clinical guidelines, meta-analyses, systematic reviews, and randomized controlled clinical trials, published between August 2005 and August 2015, in both the Portuguese and English languages was conducted. The following MeSH terms were used: ‘Lactobacillus reuteri’ and ‘Colic’. For the attribution of levels of evidence and the strength of recommendations, the Strength of Recommendation Taxonomy scale was used. Results: Fifty-nine papers were found and eight fulfilled the inclusion criteria. These included two systematic reviews and six randomized controlled clinical trials. Five of the included studies and both systematic reviews found statistically significant improvements in symptoms of colic with probiotic supplementation. One study did not demonstrate the effectiveness of Lactobacillus reuteri in infantile colic. Conclusion: There is evidence for improvement of symptoms of infantile colic with Lactobacillus reuteri supplementation, compared to placebo (Strength of Recommendation A). More good quality studies are necessary to validate these findings and to aid in the formulation of clinical guidelines.
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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 | 0.023 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".