Systematic review of clinical guidance documents for autism spectrum disorder diagnostic assessment in select regions
Bibliographic record
Abstract
Clinical guidance documents play an important role in ensuring access to high-quality autism spectrum disorder diagnostic assessment practices. The objective was to perform a systematic review of professional association and government clinical guidance documents for autism spectrum disorder diagnostic assessment, analyzing their quality and content. The government search was limited to English-speaking, single-payer, publicly funded health systems. A quality appraisal was conducted by two appraisers using the Appraisal of Guidelines Research and Evaluation, second edition tool. A content analysis was conducted for recommended clinical personnel and psychometric tools. The 11 documents demonstrated higher quality in Scope and Purpose (mean: 90.1, standard deviation: 7.4) and Clarity of Presentation (mean: 82.8, standard deviation: 9.4) and lower quality in Applicability (mean: 43.3, standard deviation: 23.8) and Rigor of Development (mean: 52, standard deviation: 21.9). All documents either recommended multidisciplinary team assessment or stated it was ideal. The documents varied substantially in their recommended tools and personnel for diagnostic assessment. There was little supporting evidence for team and personnel recommendations. Multiple guidance documents exist for autism spectrum disorder diagnostic assessments, with varying quality and recommendations. The substantial variation likely stems from insufficient evidence supporting assessment practices. Research is required to close the evidence gaps and inform high-quality clinical guidelines.
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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 | 0.029 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".