Criterion-related validity of the Test of Children's Speech sentence intelligibility measure for children with cerebral palsy and dysarthria
Bibliographic record
Abstract
PURPOSE: To evaluate the criterion-related validity of the TOCS+ sentence measure (TOCS+, Hodge, Daniels & Gotzke, 2009 ) for children with dysarthria and CP by comparing intelligibility and rate scores obtained concurrently from the TOCS+ and from a conversational sample. METHOD: Twenty children (3 to 10 years old) diagnosed with spastic cerebral palsy (CP) participated. Nineteen children also had a confirmed diagnosis of dysarthria. Children's intelligibility and speaking rate scores obtained from the TOCS+, which uses imitation of sets of randomly selected items ranging from 2-7 words (80 words in total) and from a contiguous 100-word conversational speech were compared. RESULTS: Mean intelligibility scores were 46.5% (SD = 26.4%) and 50.9% (SD = 19.1%) and mean rates in words per minute (WPM) were 90.2 (SD = 22.3) and 94.1 (SD = 25.6), respectively, for the TOCS+ and conversational samples. No significant differences were found between the two conditions for intelligibility or rate scores. Strong correlations were found between the TOCS+ and conversational samples for intelligibility (r = 0.86; p < 0.001) and WPM (r = 0.77; p < 0.001), supporting the criterion validity of the TOCS+ sentence task as a time efficient procedure for measuring intelligibility and rate in children with CP, with and without confirmed dysarthria. CONCLUSION: The results support the criterion validity of the TOCS+ sentence task as a time efficient procedure for measuring intelligibility and rate in children with CP, with and without confirmed dysarthria. Children varied in their relative performance on the two speaking tasks, reflecting the complexity of factors that influence intelligibility and rate scores.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.001 |
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".