Diagnosis of Autism Spectrum Disorder in Adolescents with Complex Clinical Presentations: A Montreal Case Series
Bibliographic record
Abstract
Background: Despite increased attention and recognition of autism spectrum disorders, many patients suffering from these disorders remain undiagnosed or are diagnosed late due to their subtle clinical presentation. The challenge for clinicians working in the field of mental health is not in screening and diagnosing young children showing typical signs of autism spectrum disorders, but rather in identifying patients at the high-functioning end of the spectrum whose intellectual abilities mask their social deficits. Objective: Because therapeutic interventions differ radically once the diagnosis of ASD has been made, it is important to understand the trajectory of those adolescents and identify clues that could help raise the diagnosis of ASD earlier. Methods: Records of eight adolescents with a late diagnosis of ASD were retrospectively reviewed to identify relevant clinical features that were overlooked in childhood and early adolescence. Results: The patients were previously misdiagnosed with multiple mental health disorders. These cases showed striking similarities in terms of developmental history, reasons for misdiagnosis, and the clinical picture at the time of ASD recognition. The cases were characterized by complex and fluctuating symptomatology, including depression, anxiety, behavioural problems, self-injurious behaviour and suicidal thoughts. Their Autism Spectrum Disorder (ASD) went previously undiagnosed due to the individual’s intelligence and learning abilities, which masked their social deficits and developmental irregularities. Signs of ASD were continuously present since childhood in all the eight cases. Once the developmental histories and the psychiatric evaluation of these adolescents were done by psychiatrists with appropriate knowledge of autism, the diagnosis of ASD was made. Conclusion: The ASD hypothesis should be raised in the presence of confusing symptoms that do not respond to usual treatment and are accompanied by an irregular developmental background. It is indeed a difficult diagnosis to make; however, the focused clinician can note subtle signs of ASD despite the intellectual learning of social codes. Family history, developmental irregularities, rigidity, difficulty in spontaneously understanding emotions, discomfort in groups and the need to be alone are significant indicators to recognize. Once the diagnosis has been considered, it must be confirmed or rejected by an experienced multidisciplinary team. The challenge for clinicians working in the field of mental health is not in screening and diagnosing young children showing typical signs of ASD, but rather in identifying patients who are at high-functioning end of the spectrum whose intellectual abilities mask their social deficits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".