A radical change in our autism research strategy is needed: Back to prototypes
Bibliographic record
Abstract
The evolution of autism diagnosis, from its discovery to its current delineation using standardized instruments, has been paralleled by a steady increase in its prevalence and heterogeneity. In clinical settings, the diagnosis of autism is now too vague to specify the type of support required by the concerned individuals. In research, the inclusion of individuals categorically defined by over-inclusive, polythetic criteria in autism cohorts results in a population whose heterogeneity runs contrary to the advancement of scientific progress. Investigating individuals sharing only a trivial resemblance produces a large-scale type-2 error (not finding differences between autistic and dominant population) rather than detecting mechanistic differences to explain their phenotypic divergences. The dimensional approach of autism proposed to cure the disease of its categorical diagnosis is plagued by the arbitrariness of the dimensions under study. Here, we argue that an emphasis on the reliability rather than specificity of diagnostic criteria and the misuse of diagnostic instruments, which ignore the recognition of a prototype, leads to confound autism with the entire range of neurodevelopmental conditions and personality variants. We propose centering research on cohorts in which individuals are selected based on their expert judged prototypicality to advance the theoretical and practical pervasive issues pertaining to autism diagnostic thresholds. Reversing the current research strategy by giving more weight to specificity than reliability should increase our ability to discover the mechanisms of autism. LAY SUMMARY: Scientific research into the causes of autism and its mechanisms is carried out on large cohorts of people who are less and less different from the general population. This historical trend may explain the poor harvest of results obtained. Services and intervention are provided according to a diagnosis that now encompasses extremely different individuals. Last, we accept as a biological reality the constant increase over the years in the proportion of autistic people among the general population. These drifts are made possible by the attribution of a diagnosis of autism to people who meet vague criteria, rather than to people who experienced clinicians recognize as autistic. We propose to change our research strategy by focusing on the study of the latter, fewer in number, but more representative of the "prototype" of autism. To do this, it is necessary to clearly distinguish the population on which the research is carried out from that to which we provide support. People must receive services according to their needs, and not according to the clarity of their diagnosis.
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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 | 0.172 | 0.129 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.007 | 0.079 |
| Scholarly communication | 0.022 | 0.069 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.011 | 0.052 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".