Analysis of Clinical Features Predicting Etiologic Yield in the Assessment of Global Developmental Delay
Bibliographic record
Abstract
OBJECTIVE: Global developmental delay is a common reason for presentation for neurologic evaluation. This study examined the role of clinical features in predicting the identification of an underlying cause for a child's global developmental delay. METHODS: Over a 10-year inclusive interval, the case records of all consecutive children <5 years of age referred to a single ambulatory practice setting for global developmental delay were systematically reviewed. The use of clinical features in predicting the identification of a specific underlying cause for a child's delay was tested using chi2 analysis. RESULTS: A total of 261 patients eventually met criteria for study inclusion. Mean age at initial evaluation was 33.6 months. An underlying cause was found in 98 children. Commonest etiologic groupings were genetic syndrome/chromosomal abnormality, intrapartum asphyxia, cerebral dysgenesis, psychosocial deprivation, and toxin exposure. Factors associated with the ability to eventually identify an underlying cause included female gender (40 of 68 vs 58 of 193), abnormal prenatal/perinatal history (52 of 85 vs 46 of 176), absence of autistic features (85 of 159 vs 13 of 102), presence of microcephaly (26 of 40 vs 72 of 221), abnormal neurologic examination (52 of 71 vs 46 of 190), and dysmorphic features (44 of 84 vs 54 of 177). In 113 children without any abnormal features identified on history or physical examination, routine screening investigations (karyotype, fragile X molecular genotyping, and neuroimaging) revealed an underlying etiology in 18. CONCLUSIONS: Etiologic yield in an unselected series of young children with global developmental delay is close to 40% overall and 55% in the absence of any coexisting autistic features. Clinical features are readily apparent that may enhance an expectation of a successful etiologic search. Screening investigations may yield an underlying cause.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".