Autism Screening Tests: A Narrative Review
Bibliographic record
Abstract
As screening tests are tools to quantify communication-interactive abilities of speech and language; therefore, to evaluate, screen, diagnose and treat various aspects of one's abilities, they are necessary. The purpose of this study is to review the existing autism screening tools, their subtests, administration, scoring, and application in clinical and research contexts in children and adults. This study was a review of autism screening tools; hence, an electronic search through databases such as PubMed, Scopus, Medline, SID, and Magiran was performed from 2000 to 2021. The tests were examined in terms of year of publication, duration, age range, assessment method, subtests, and psychometric properties and furthermore, they were reviewed in details. In this study, 19 autism screening tests were evaluated and The Autism Spectrum Quotient was found to have the shortest administration time while The Gilliam Autism Rating Scale had the longest, and the only test that varied in duration was the Autism Screening Instrument for educational planning. Autism screening is a complex issue. Reviewing these articles reveals that some tests have been used more in recent years due to their specialized subtests or easy and fast administration. Prompt testing is extremely crucial especially in emergency situations like the current COVID-19 pandemic the world is struggling with today. A review of speech tone tests shows that the CARS-2 is one of the most widely validated autism assessments.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".