Autism, Diagnostics, and Dementia: A Consensus Report From the 2nd International Summit on Intellectual Disabilities and Dementia
Bibliographic record
Abstract
OBJECTIVES: The second International Summit on Intellectual Disability and Dementia, held in 2023, highlighted the unique challenges of diagnosing dementia in older autistic adults, particularly those with intellectual disabilities, due to the complex interplay of cognitive, communicative, and behavioral factors. This article addresses key diagnostic issues and post-diagnostic considerations for this population. METHOD: A consensus report was developed by the Summit's Autism/Dementia Working Group through background reviews, expert discussions at the Summit, and iterative draft revisions, incorporating feedback from internal and external stakeholders. Key issues were extracted from the report and abridged for this manuscript. RESULTS: Diagnostic challenges stem from overlapping symptoms of co-occurring neurodevelopmental and psychiatric conditions, rendering standard dementia tools insufficient. Comprehensive evaluations tailored to autism-related traits, sensory sensitivities, and alternative communication methods are essential. Building diagnostic capacity among clinicians and fostering multidisciplinary collaboration are critical. Longitudinal assessments, initiated before dementia symptoms appear, facilitate early detection of subtle changes. Emerging biomarkers and neuroimaging techniques show promise and should be incorporated where feasible. Accommodations, such as virtual assessments in familiar settings, can enhance diagnostic accuracy by reducing anxiety. Creating transition processes from diagnostics to post-diagnostic supports will aid in mitigating challenges and enhance life quality when dementia is a factor. CONCLUSIONS: Research and clinician education are urgently needed to improve diagnostic approaches and streamline the transition from diagnosis to tailored post-diagnostic support. An integrated framework of comprehensive efforts is vital for our better understanding of age-associated neuropathological diagnostics and enabling long-term well-being of older autistic adults with dementia.
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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 | 0.204 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".