Prediction of the outcome of children who had a language delay at age 2 when they are aged 4: Still a challenge
Bibliographic record
Abstract
PURPOSE: This study investigated the role that variables related to children and their environment play in the prediction of outcomes at 4 years of age for children with a language delay at 2 years. METHOD: A longitudinal study was undertaken where 64 children (45 boys, 19 girls; mean age = 53.3 months; SD = 4.4) with language delay at age 2 years were re-evaluated at age 4 years. Three developmental trajectories were analysed. RESULT: The early stages of grammar, as estimated by mean length of utterance at 3.5 years, are an important prognosis factor of subsequent language impairment (LI). Children who are exposed to several risk factors simultaneously are more likely to have a language delay (LD) or a LI, but the profile of LD children is more akin to that of the typically developing (TD) children. Children with LI tend to have profiles with a greater number of risk factors. CONCLUSION: The results of this study encourage different intervention approaches depending on the child's language profile at 2 years, due to differing language prognosis. The results also point to the need to assess the child's environment. Future studies with large diverse population samples may give more precise information on potential risk factors and their cumulative effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".