Evidence map›Paper›PMID 42325991›Full record

ArticleFrontiers in psychiatry2026

Multimodal evidence and technology-enabled accommodations for autistic adults: an evidential framework for autism research and practice.

David Ruttenberg

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

David RuttenbergUniversity College London, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autistic adults often face challenges to the legitimacy of their own neurology, in part because narrowly deficit-centric frameworks continue to privilege externally observed impairment over converging evidence from neuroscience, physiology, lived experience, and technology. This article proposes evidential pluralism - grounded in the Russo-Williamson thesis that causal inference in the health sciences requires the integration of difference-making and mechanistic evidence - as a framework for understanding autistic adulthood, extended here to encompass five distinct claim types: phenomenological, mechanistic, diagnostic, intervention, and ethical or policy. Drawing on converging but heterogeneous evidence from cognitive and sensory neuroscience, research on masking and mental health, gender and gender diversity, participatory methods, and wearable-technology studies, the paper considers how improved recognition of neurobiological diversity may have contributed to spectrum expansion, while acknowledging the parallel roles of diagnostic broadening, ascertainment effects, instrument drift, and sex and gender-related diagnostic bias. The paper explores how wearable and AI-enabled accommodation systems might, in principle, support autistic adults in real time and generate longitudinal physiological data, while emphasising the need for rigorous validation, independent replication, and robust privacy and data-governance safeguards. A dedicated section addresses the full heterogeneity of autistic adulthood, including adults with the highest support needs. The article closes by outlining implications for research design, clinical practice, and policy, framing accommodations as a matter of rights rather than discretionary privilege.

Indexed as

autism spectrumcamouflaginggender diversitymotor ASDneurodiversityparticipatory researchsensory accommodationswearable technologies

Identifiers

PMID42325991
PMCPMC13275420

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.