Equity, acceptability and feasibility of using polyunsaturated fatty acids in children and adolescents with autism spectrum disorder: a rapid systematic review
Bibliographic record
Abstract
INTRODUCTION: Some recent randomized controlled trials (RCTs) assessed the efficacy and safety of polyunsaturated fatty acids (PUFAs) for the treatment of autism spectrum disorder (ASD). To optimally inform the Italian guideline for the management of ASD in children and adolescents, we reviewed the impact on equity, acceptability and feasibility for developing a pilot recommendation for PUFAs. METHODS: We performed a rapid systematic review of observational and experimental studies on PUFAs for children and adolescents with ASD, extracting data on resources required, equity, acceptability, and feasibility of PUFAs. We followed the framework provided by the grading of recommendations assessment, development and evaluation (GRADE) methodology, and we assessed risk of bias and methodological quality of included studies. Results were synthesized both narratively and quantitatively to address clinically relevant questions on equity, acceptability, and feasibility. RESULTS: We found 14 papers related to equity. PUFAs did not seem to impact equity importantly. We did not find variation in effectiveness across subgroups and in a base case scenario, the cost of a 12 weeks cycle of therapy with 1.155 g/day of PUFAs was €65.51 euro. The acceptability of PUFAs was evaluated in 17 studies, 9 of which were RCTs. PUFAs were widely used among children and adolescents with ASD (18 to 51%), and 50% of parents considered nutritional supplementation as useful. Difficulty in swallowing capsules and bad taste were identified as possible causes of poor compliance, but treatment adherence, when measured in included RCTs, was judged to be good to excellent. Discontinuation due to any cause for PUFAs could not differ from placebo (low certainty of evidence). The feasibility of using PUFAs was assessed in 12 studies. PUFAs were probably sustainable, and no particular critical issue emerged from the feasibility assessment. However, the evidence appeared scarce and indirect. CONCLUSIONS: We found the administration of PUFAs in children and adolescents with ASD to be potentially equitable, acceptable and feasible. These results are limited by the limited number and quality of retrieved documents, and need to be viewed in light of efficacy and safety data to formulate clinical recommendations.
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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 | 0.091 | 0.305 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| 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".