Complementary, alternative and integrative medicine for autism: an umbrella review and online platform
Bibliographic record
Abstract
The use of complementary, alternative and integrative medicine (CAIM) is highly prevalent among autistic individuals, with up to 90% reporting having used CAIM at least once in their lifetime. However, the evidence base for the effects of CAIM for autism remains uncertain. Here, to fill this gap, we conducted an umbrella review of meta-analyses exploring the effects of CAIM in autism across the lifespan and developed a web platform to disseminate the generated results. Five databases were searched (up to 31 December 2023) for systematic reviews with meta-analyses exploring the effects of CAIM in autism. Independent pairs of investigators identified eligible papers and extracted relevant data. Included meta-analyses were reestimated using a consistent statistical approach, and their methodological quality was assessed with AMSTAR-2. The certainty of evidence generated by each meta-analysis was appraised using an algorithmic version of the GRADE framework. This process led to the identification of 53 meta-analytic reports, enabling us to conduct 248 meta-analyses exploring the effects of 19 CAIMs in autism. We found no high-quality evidence to support the efficacy of any CAIM for core or associated symptoms of autism. Although several CAIMs showed promising results, they were supported by very low-quality evidence. The safety of CAIMs has rarely been evaluated, making it a crucial area for future research. To support evidence-based consideration of CAIM interventions for autism, we developed an interactive platform that facilitates access to and interpretation of the present results ( https://ebiact-database.com ).
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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 | 0.035 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.039 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.008 |
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".