The Americleft Speech Project: A Training and Reliability Study
Bibliographic record
Abstract
OBJECTIVE: To describe the results of two reliability studies and to assess the effect of training on interrater reliability scores. DESIGN: The first study (1) examined interrater and intrarater reliability scores (weighted and unweighted kappas) and (2) compared interrater reliability scores before and after training on the use of the Cleft Audit Protocol for Speech-Augmented (CAPS-A) with British English-speaking children. The second study examined interrater and intrarater reliability on a modified version of the CAPS-A (CAPS-A Americleft Modification) with American and Canadian English-speaking children. Finally, comparisons were made between the interrater and intrarater reliability scores obtained for Study 1 and Study 2. PARTICIPANTS: The participants were speech-language pathologists from the Americleft Speech Project. RESULTS: In Study 1, interrater reliability scores improved for 6 of the 13 parameters following training on the CAPS-A protocol. Comparison of the reliability results for the two studies indicated lower scores for Study 2 compared with Study 1. However, this appeared to be an artifact of the kappa statistic that occurred due to insufficient variability in the reliability samples for Study 2. When percent agreement scores were also calculated, the ratings appeared similar across Study 1 and Study 2. CONCLUSION: The findings of this study suggested that improvements in interrater reliability could be obtained following a program of systematic training. However, improvements were not uniform across all parameters. Acceptable levels of reliability were achieved for those parameters most important for evaluation of velopharyngeal function.
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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 | 0.032 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".