Children With a History of Expressive Vocabulary Delay
Bibliographic record
Abstract
Outcomes of 21 children who were previously identified as late talkers were investigated at 5 years of age. The model of service delivery used for these children included a parent program for preventive intervention when the children were 2 years old, followed by focused direct intervention for children whose gains in speech and/or language skills continued to be slow. Their outcomes at 5 years of age were investigated using general language measures as well as higher level language tasks designed to stress the language system. Late talkers’ results were compared to those of a comparison group of children with histories of typical language development. Scores on standardized tests of language development indicated that the majority of late-talking children (i.e., 86%) had ‘caught up’ to their age-matched peers in expressive grammar and vocabulary. However, weaknesses remained in a number of higher level language areas, including a standardized test designed to measure facility with teacher-child discourse, a novel task that examined the child's use of pragmatic cues for anaphora resolution of ambiguous sentences, and narrative tasks. The clinical implications of these findings include close monitoring of these children as they reach school age and intervention in key areas of weakness for children who continue to demonstrate language difficulties as they mature.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".