Meta-analysis of flavonoids for the treatment of haemorrhoids
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to evaluate the impact of flavonoids on those symptoms important to patients with symptomatic haemorrhoids. METHODS: A comprehensive search strategy was used. All published and unpublished randomized controlled trials comparing any type of flavonoid to placebo or no therapy in patients with symptomatic haemorrhoids were included. Two reviewers independently screened studies for inclusion, retrieved all potentially relevant studies and extracted data. RESULTS: Fourteen eligible trials randomized 1514 patients. Studies were of moderate quality and showed variability in the results with potential publication bias. Meta-analyses using random-effects models suggested that flavonoids decrease the risk of not improving or persisting symptoms by 58 per cent (relative risk (RR) 0.42 (95 per cent confidence interval (c.i.) 0.28 to 0.61)) and showed an apparent reduction in the risk of bleeding (RR 0.33 (95 per cent c.i. 0.19 to 0.57)), persistent pain (RR 0.35 (95 per cent c.i. 0.18 to 0.69)), itching (RR 0.65 (95 per cent c.i. 0.44 to 0.97)) and recurrence (RR 0.53 (95 per cent c.i. 0.41 to 0.69)). CONCLUSION: Limitations in methodological quality, heterogeneity and potential publication bias raise questions about the apparent beneficial effects of flavonoids in the treatment of haemorrhoids.
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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 | 0.025 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".