Sensitivity and Specificity of French Language and Processing Measures for the Identification of Primary Language Impairment at Age 5
Bibliographic record
Abstract
PURPOSE: Research on the diagnostic accuracy of different language measures has focused primarily on English. This study examined the sensitivity and specificity of a range of measures of language knowledge and language processing for the identification of primary language impairment (PLI) in French-speaking children. Because of the lack of well-documented language measures in French, it is difficult to accurately identify affected children, and thus research in this area is impeded. METHOD: The performance of 14 monolingual French-speaking children with confirmed, clinically identified PLI (M = 61.4 months of age, SD = 7.2 months) on a range of language and language processing measures was compared with the performance of 78 children with confirmed typical language development (M age = 58.9 months, SD = 5.7). These included evaluations of receptive vocabulary, receptive grammar, spontaneous language, narrative production, nonword repetition, sentence imitation, following directions, rapid automatized naming, and digit span. Sensitivity, specificity, and likelihood ratios were determined at 3 cutoff points: (a) -1 SD, (b) -1.28 SD, and (b) -2 SD below mean values. Receiver operator characteristic curves were used to identify the most accurate cutoff for each measure. RESULTS: Significant differences between the PLI and typical language development groups were found for the majority of the language measures, with moderate to large effect sizes. The measures differed in their sensitivity and specificity, as well as in which cutoff point provided the most accurate decision. Ideal cutoff points were in most cases between the mean and -1 SD. Sentence imitation and following directions appeared to be the most accurate measures. CONCLUSIONS: This study provides evidence that standardized measures of language and language processing provide accurate identification of PLI in French. The results are strikingly similar to previous results for English, suggesting that in spite of structural differences between the languages, PLI in both languages involves a generalized language delay across linguistic domains, which can be identified in a similar way using existing standardized measures.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".