How do we define normal bowel frequency from newborn to teens?: A Bayesian meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: Defecation disorders are a common pediatric problem and bowel frequency is crucial in identifying them. The aim of this analysis is to define normal bowel frequencies in healthy children ranging from newborns to adolescents. METHODS: A literature search was conducted using MEDLINE, SCOPUS, EMBASE, Cochrane Library, and Web of Science from their inception to February 2024, aiming to identify studies reporting bowel habits of healthy children (0-18 years). A Bayesian distribution modeling approach was adopted to pool the mean frequency of bowel opening using inverse-variance weighing. A subgroup analysis and a meta-regression were performed with Bayesian generalized additive mixed distributional models. The methodological quality of the articles was evaluated using the Newcastle-Ottawa Scale modified for cross-sectional studies. RESULTS: Seventeen studies were included in the analysis, including 22,698 children aged from 0 to 18 years. The subgroup meta-analysis showed mean bowel frequencies for newborns, 1-6 months, 6-12 months, 1-2 years, 2-5 years, and over 5 years are 3.24 (95% credible interval [CrI]: 2.83-3.63), 1.99 (95% CrI: 1.77-2.19), 1.66 (95% CrI: 1.45-1.88), 1.53 (95% CrI: 1.37-1.7), 1.15 (95% CrI: 0.99-1.31), and 1.02 (95% CrI 0.88-1.18), respectively. Between studies, heterogeneity demonstrated a near-normal distribution with a mean of 0.16 and a 95% CrI of 0.04-0.28. The variance of the distribution of mean bowel frequency reduced with age. DISCUSSION: In this Bayesian meta-analysis, we found that younger children have a higher bowel frequency. The reported bowel frequencies for each age group could serve as normal values in clinical practice to differentiate health and disease.
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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 | 0.070 | 0.144 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.047 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".