Liens entre troubles du spectre autistique et schizophrénies précoces
Bibliographic record
Abstract
Context : Autism spectrum disorders (ASD) , early onset schizophrenia (EOS) or even very early onset schizophrenia (VEOS)... so many terms used these days! Although today, classifications are categorical, work is however increasing to deepen and understand a possible link between ASD and schizophrenia spectrum disorders, and especially a link between ASD and early onset schizophrenia. Objective : To clarify clinical and biological links between ASD and EOS (before age 18) and especially links between ASD and VEOS (before age 13). Méthod: 62 subjects, divided into three groups according to age at onset of schizophrenia : strictly before age 13 (VEOS ), between age 13 and 18 (EOS) and after 18 (Adult Onset Schizophrenia, AOS). For each group, two clinical evaluations are assessed: search early symptoms of autism with the ADI-R (Autism Diagnostic Interview-Revised) and phenotypic evaluation with MINI (Mini International Neuropsychiatric Interview), BPRS (Brief Psychiatric Rating Scale), PANSS (Positive And Negative Symptoms Scale for schizophrenia), STAI (State Trait Anxiety Inventory), TAS (Toronto Alexithymia Scale) et NSS (Neurological Soft Signs). Biological analysis is based on salivary cortisol measurements, collected during a 24-h period (0800h-day 1, 1100h, 1600h, 2400h, 0800h-day2), in order to evaluate the stress response of the hypothalamic-pituitary-adrenal axis. Résults: VEOS symptoms > EOS symptoms > AOS symtoms. The earlier the schizophrenia is and the more present premorbid history of autism is and the more abnormal stress response is (abnormal stress response also present in relatives). Conclusion: A clinico-biological link appears between VEOS and ASD, with early symtoms of autism before thirty six months and stress response abnormalities. Are VEOS different from schizophrenia? Anyway, a diagnosis to know better in order to improve patients and families cares.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".