Treatment intensity and childhood apraxia of speech
Bibliographic record
Abstract
BACKGROUND: Intensive treatment has been repeatedly recommended for the treatment of speech deficits in childhood apraxia of speech (CAS). However, differences in treatment outcomes as a function of treatment intensity have not been systematically studied in this population. AIM: To investigate the effects of treatment intensity on outcome measures related to articulation, functional communication and speech intelligibility for children with CAS undergoing individual motor speech intervention. METHODS & PROCEDURES: A total of 37 children (32-54 months of age) with CAS received 1×/week (lower intensity) or 2×/week (higher intensity) individual motor speech treatment for 10 weeks. Assessments were carried out before and after a 10-week treatment block to study the effects of variations in treatment intensity on the outcome measures. OUTCOMES & RESULTS: The results indicated that only higher intensity treatment (2×/week) led to significantly better outcomes for articulation and functional communication compared with 1×/week (lower intensity) intervention. Further, neither lower nor higher intensity treatment yielded a significant change for speech intelligibility at the word or sentence level. In general, effect sizes for the higher intensity treatment groups were larger for most variables compared with the lower intensity treatment group. CONCLUSIONS & IMPLICATIONS: Overall, the results of the current study may allow for modification of service delivery and facilitate the development of an evidence-based care pathway for children with CAS.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".