Multiple sclerosis clinical practice guidelines provide few complementary and alternative medicine recommendations: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Complementary and alternative medicine (CAM) use is prevalent among individuals with multiple sclerosis (MS), yet the quantity and quality of CAM recommendations in MS clinical practice guidelines (CPGs) has not been assessed. The objective of this study was to determine the mention of CAM in MS CPGs and assess the quality of CAM recommendations. DESIGN/SETTING: A systematic review was conducted to identify MS CPGs. MEDLINE, EMBASE and CINAHL were searched from 2008 to 2018. The Guidelines International Network and the National Center for Complementary and Integrative Health (NCCIH) websites were also searched. Eligible CPGs containing CAM recommendations published by non-profit agencies on the treatment of MS for adults were assessed for quality and reporting using the Appraisal of Guidelines, Research and Evaluation II (AGREE II) instrument. OUTCOME/RESULTS: From 204 unique search results, six CPGs mentioned CAM and four made CAM recommendations. Scaled domain percentages from highest to lowest were clarity of presentation (90.3 % Overall, 83.3 % CAM), scope and purpose (87.5 % Overall, 86.8 % CAM), rigour of development (80.0 % Overall, 61.7 % CAM), applicability (55.2 % Overall, 44.3 % CAM), editorial independence (49.0 % Overall, 47.9 % CAM), and stakeholder involvement (55.6 % Overall, 39.6 % CAM). Quality varied within and across CPGs. Three of the four CPGs were recommended by both appraisers; one was recommended as "No" or "Yes with modifications". CONCLUSION: CAM recommendations were only present in one third of all eligible CPGs. CPGs that scored highly can be used by patients and healthcare professionals as the basis for discussion about the use of CAM therapies for MS treatment/management. Although many people living with MS (PwMS) seek CAM therapies, few CPGs are available to provide guidance for clinicians and patients.
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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 | 0.036 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| 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".