Individual and cumulative risk factors in developmental language disorder: A case-control study
Bibliographic record
Abstract
Many mental disorders (MD) share common etiology, fuelling debates about the specificity of clinical categories and whether the presence of specific risk factors (RF) can distinguish among them. The study of developmental language disorder (DLD), more specifically, has been further hindered by a lack of consensus regarding its definition. These limitations increase the risk of under-detection and lifelong consequences for affected children. This paper aims (1) to document which individual RF allow differentiating DLD from other MD and (2) to compare the cumulative RF between children with DLD versus other MD. This case-control design study used medical records of a psychiatric sample of 795 preschoolers (mean age 4:11, 75% boys). A logistic regression measured the predictive value of potential RF on DLD. Later first sentences, maternal immigration and family history of language delay were identified as significant in explaining 30% of the variance for DLD diagnosis. An ANCOVA revealed that children with DLD were exposed to a significantly higher number of RF than were children with other MD. Public health policies informed with the knowledge of specific RF associated with DLD, and their cumulative impact, could improve early detection and reduce the cascade of negative consequences associated with DLD.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".