Clinical Situations in Which the Diagnosis of Autism is Debatable: An Analysis and Recommendations
Bibliographic record
Abstract
The "autism spectrum disorder" (ASD) construct and its current diagnostic criteria have led to the inclusion of increasingly heterogeneous and decreasingly atypical individuals under its definition. This broad category, based on the polymorphic clinical expression of common genetic variants underpinning the risk of autism, is likely beneficial for certain individuals. However, determining the boundaries between ASD and typical individuals, as well as those with other neurodevelopmental conditions, remains an issue of which the importance is growing with the increase in ASD prevalence. We identified four clinical contexts associated with a questionable, poorly justified, or unhelpful ASD diagnosis: (1) those in which diagnostic instruments raise uncertainties, (2) in the context of a subclinical presentation, (3) when early autistic signs tend to fade away during development, and (4) when comorbidities are prominent. We argue that in certain cases, a diagnosis of ASD may not be the most suitable, timely, or helpful medical act and provide recommendations for clinical practice when facing such situations.
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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 | 0.022 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".