Screening for Autism in Preterm Children: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Preterm children exhibit a higher prevalence of autism spectrum disorder (ASD) than the general population. The unique neurodevelopmental characteristics of preterm children present challenges in screening for and diagnosing ASD. To date, a systematic review of screening tools for ASD in this population has not been completed. This systematic review and meta-analysis evaluates the diagnostic performance of currently used ASD screening tools in the preterm population. METHODS: The database search was conducted by using MEDLINE, PsycINFO, PubMed, Embase, and CINAHL in July 2024. Articles that quantified the diagnostic accuracy of ASD screening tools in the preterm population were included. Nine studies were included in this review, and only 4 studies in the meta-analyses. All studies were assessed for risk of bias, applicability, and certainty. RESULTS: Sensitivity of screening tools for ASD in preterm children ranged from 0% to 100%, whereas specificity ranged from 38% to 98%. Pooled data were available for the Modified Checklist for Autism in Toddlers (2 studies) and Social Communication Questionnaire. (2 studies), with pooled sensitivities of 55% and 53% and specificities 85% and 90%, respectively. CONCLUSIONS: There was significant study heterogeneity, limiting the number of studies from which to pool diagnostic accuracy data. Screening tools vary in their ability to identify ASD in the preterm population, underscoring how overlapping behavioral phenotypes may confound early identification. There is a critical need to refine and assess ASD screening tools in preterm children, facilitating timely interventions in this cohort.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".