Identification of Children With Language Impairment: Investigating the Classification Accuracy of the MacArthur–Bates Communicative Development Inventories, Level III
Bibliographic record
Abstract
PURPOSE: This study tested the accuracy with which the MacArthur-Bates Communicative Development Inventories, Level III (CDI-III), a parent report measure of language ability, discriminated children with language impairment from those developing language typically. METHOD: Parents of 58 children, 49 with typically developing language (age 30 to 42 months) and 9 with language impairment (age 31 to 45 months) completed the CDI-III, a 2-page questionnaire that includes 100 vocabulary items, 12 sentence pairs, and 12 questions regarding linguistic concepts. RESULTS: A discriminant analysis indicated that the CDI-III total score together with age classified children into language status groups with 96.6% accuracy overall. The corresponding likelihood ratios supported this strong level of accuracy, although precision may not be as high as indicated by broad confidence intervals. CONCLUSIONS: Results of this study contribute to the accumulating evidence on the types of valid inferences that may be made from the CDI-III, specifically its classification accuracy. Further research should continue to investigate classification accuracy in larger samples with broader maternal education levels and with different types of language impairments. Additional research should also investigate the classification accuracy when the CDI-III is used in combination with other tests.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".