Complementary and alternative medicine mention and recommendations in inflammatory bowel disease guidelines: systematic review and assessment using AGREE II
Bibliographic record
Abstract
Abstract Background Many patients with inflammatory bowel disease (IBD) use complementary and alternative medicine (CAM) for disease management. There is, however, a communication gap between patients and healthcare professionals regarding CAM use, where patients are hesitant to disclose CAM use to providers. The purpose of this study was to identify the quantity and assess the quality of CAM recommendations in IBD clinical practice guidelines (CPGs) using the Appraisal of Guidelines for Research and Evaluation II (AGREE II) instrument. Methods MEDLINE, EMBASE, and CINAHL were systematically searched from 2011 to 2022 to find CPGs for the treatment and/or management of IBD. The Guidelines International Network (GIN) and National Center for Complementary and Integrative Health (NCCIH) websites were also searched. Eligible CPGs were assessed using the AGREE II instrument. Results Nineteen CPGs made CAM recommendations for IBD and were included in this review. Average scaled domain percentages of CPGs were as follows (overall CPG, CAM section): scope and purpose (91.5%, 91.5%), clarity of presentation (90.3%, 64.0%), editorial independence (57.0%, 57.0%), stakeholder involvement (56.7%, 27.8%), rigour of development (54.7%, 45.9%), and applicability (14.6%, 2.1%). Conclusions The majority of CPGs with CAM recommendations were of low quality and their CAM sections scored substantially lower relative to other therapies in the overall CPG. In future updates, CPGs with low scaled-domain percentages could be improved in accordance with AGREE II and other guideline development resources. Further research investigating how CAM therapies can best be incorporated into IBD CPGs is warranted.
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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 | 0.083 | 0.312 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.034 | 0.030 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".