Validation of Differential Diagnosis of Autism Spectrum Disorder and Intellectual Disability Scale in Pakistan
Bibliographic record
Abstract
Due to the recent clinical work's growing awareness of both autistic spectrum disorder (ASD) and intellectual disability (ID), differentiating between the two diagnoses is becoming more difficult, especially in developing countries like Pakistan. The co-occurrence of symptoms and characteristics contributed to the confusion. This tool attempts to address this challenge. From the parents of 20 people, 10 of whom were diagnosed with ASD and 10 of whom were diagnosed with ID, a total of 92 symptoms and traits were elicited, using the phenomenological method. Ten professional psychiatrists and clinical psychologists validated the explored signs diagnostically associated with ASD or ID. The resulting 66 symptoms fit firmly into one of the two categories. Two hundred sixty (260) mothers or teachers of children diagnosed with ASD (n=110) or ID (n=150) were interviewed, using base ratings. The statistical analyses indicated 36 features with high factor loading and statistical significance for ASD and 7 characteristics for ID. The tool named ‘Differential Diagnosis of Autism and Intellectual Disability (DDAID) Scale’ showed respectable sensitivity, specificity, positive predictive, and concurrent validity values (78%, 89%, 86%, & 89% respectively). The findings were reviewed for their cultural ramifications, enhancements in differential diagnosis, and their usefulness in creating training programs for certain people.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".