A241 KIWIFRUIT AND KIWIFRUIT EXTRACTS FOR TREATMENT OF CONSTIPATION: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Aims This systematic review and meta-analysis aimed to determine the effectiveness of kiwifruit or kiwifruit extracts in thetreatment of constipation. Methods Electronic databases were searched from inception until January 2021. Eligible studies enrolled participants with functional constipation (FC) or irritable bowel syndrome with constipation (IBS-C) with randomization to receive kiwifruit or kiwifruit extracts vs. any non-kiwifruit control. Standardized meandifference (SMD) or mean difference (MD) with confidence intervals (CI) were determined for the following outcomes: weekly frequency of spontaneous bowel movements (SBM) and Bristol Stool Scale (BSS). GRADE approach was used to rate the certainty of evidence. PROSPERO registration number CRD42020207365. Results Seven randomized controlled trials including 399 participants (82% female; mean age 42 years [Standard Deviation 14.6]) were included. Compared with placebo, kiwifruit extract might increase weekly frequency of SBM (MD 1.36; 95% CI -0.44 to 3.16) with low certainty evidence. Kiwifruit had an uncertain effecton BSS (SMD 1.54; 95% CI -1.33 to 4.41) with very low certainty evidence. Compared with psyllium, kiwifruitmay increase weekly SBM (MD 1.1; 95% CI -0.02 to 2.04) and may increase BSS (softer stools) (MD 0.63;95% CI 0.01 to 1.25) both with low certainty evidence. Compared to placebo, kiwifruit encapsulated extracts may result in an increase in minor adverse events (Relative Risk 4.58; 95% CI 0.79 to 26.4). Conclusions Among individuals with constipation, overall low certainty of evidence indicate that kiwifruit may increase SBM when compared to placebo or psyllium. Although overall results are promising, establishing therole of kiwifruit in FC or IBS-C requires large, methodologically rigorous trials. Funding Agencies None
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".