Building Consensus Around Adult Autism Diagnostic Assessment: A Mixed-Methods Approach
Bibliographic record
Abstract
Background: Diagnosing autism in adulthood presents several challenges, including scant validated measures, overlapping characteristics with other conditions, and limited information regarding client preferences and experiences. We sought to generate consensus best practice (CBP, i.e., high endorsement or agreement by ≥75%) between expert clinicians and autistic adults around existing clinical practice guidelines (CPGs) and co-develop statements for enhancing practice. Methods: Thirty-five autistic adults and 30 expert clinicians participated in a three-round Delphi survey. Participants rated abbreviated versions of National Institute for Health and Care Excellence CPGs for adult autism diagnosis on importance, feasibility, and acceptability. In Round 1, participants also answered open-ended questions regarding what is currently working well, missing, and in need of improvement in adult autism assessment. We conducted thematic analysis on these open-ended responses to generate co-developed statements. In subsequent rounds, participants reviewed prior round ratings and comments and then re-rated CPGs (on all three metrics) and co-developed statements to generate CBP. They also selected their top five co-developed statements in Round 3. Results: By Round 3, both groups reached CBP on the importance of over 50% of CPGs, covering topics such as having trained professionals and assessing core autism features, mental health, and early development. However, they reached CBP less often on feasibility and acceptability. Clinicians reached CBP more often than autistic adults. Participants also reached CBP on most (24/31) co-developed statements, highlighting priorities like clear communication, neurodiversity-affirming care, and better access to diagnostic assessments. Participants did not reach CBP for informant involvement within the autistic adult group or for multidisciplinary evaluations across groups. Conclusions: While participants supported the importance of many CPGs, further research should address feasibility and acceptability challenges. CBP and co-developed statements may enhance adult autism assessments for both clinicians and autistic adults; however, empirical evaluation in clinical settings will be key.
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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 | 0.439 | 0.378 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".