Effectiveness of the Corrective Babbling Speech Treatment Program for Children with a History of Cleft Palate or Velopharyngeal Dysfunction
Bibliographic record
Abstract
OBJECTIVE: To investigate the effectiveness of a phonetic-based, parent-administered treatment to reduce cleft-related speech errors. DESIGN: Immediate treatment (IT) versus delayed treatment (DT) comparison followed by analysis of gains over approximately 4 months and again after a longer period of treatment for participants in both groups. PARTICIPANTS: Thirteen participants with a history of cleft palate or velopharyngeal dysfunction and speech disorder. INTERVENTION: After training, parents were instructed to conduct daily 10-minute sessions of phonetic drills with their children. MAIN OUTCOME MEASURES: Group differences in percent consonants correct (PCC) were compared between those who did and those who did not receive the treatment. Pre- and posttreatment PCC comparisons were made after approximately 4 months of treatment and after termination of treatment. RESULTS: Participants receiving Corrective Babbling therapy showed significant gains in PCC in limited contexts after 4 months of therapy. However, gains were not significantly greater for the IT versus DT group. Participants who continued with the treatment for longer than 4 months showed the greatest gains in PCC and had significant improvements in all speech contexts assessed. Parents reported satisfaction with the program. CONCLUSIONS: The results of this study indicate that this treatment shows promise for at least some children with a history of cleft palate and velopharyngeal dysfunction.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".