Bibliographic record
Abstract
Introduction The hallmark social deficits of ASD are: 1. Poor eye contact 2. Limited emotional, facial and voice expression 3. Deficits in joint attention including deficits in showing and seeking recognition 4. Deficits in mutual enjoyment 5. Deficits in reading body language These social deficits can be difficult to identify and to define even by trained clinicians, especially when there are multiple diagnoses, and other psychiatric disorders may have/mimic these social deficits. Some of the social deficits characteristic of an ASD are found in conditions that are not ASD. The social deficits found in some children with ADHD are amplfied when the ADHD is co-morbid with Social Anxiety disorders, Language disorders, OCD and/or Global Developmental Delay, making these children look “almost autistic”. Objective To demonstrate the difficulty in trying to differentiate the “almost autistic” child from a child with ASD. Aim To stimulate discussion over the difficulty of diagnosing ASD and differentiating ASD from other childhood psychiatric disorders. Method A Poster presentation with a video has been developed demonstrating “difficult to diagnose” children, and raising the alternative diagnoses. Results The distinction between ADHD, ASD, and other disorders and not clear Conclusions The prevalence of ASD has mushroomed in the last 20 years, partly because of greater awareness, but also because of criteria have been diffused to include other diagnostic groups. Until better diagnostic approaches occur, the clinician is dependent on observational strategies to make the diagnosis of ASD. Given the importance of the diagnosis, better techniques for diagnosis is required.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.752 | 0.460 |
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".