What is the optimal assessment of speech? A multicentre, international evaluation of speech assessment in 2500 patients with a cleft
Bibliographic record
Abstract
Objectives Speech problems in patients with a cleft palate are often complex and multifactorial. Finding the optimal way of monitoring these problems is challenging. The International Consortium of Health Outcomes Measurement (ICHOM) has developed a set of standardised outcome measures at specific ages for patients with a cleft lip and/or palate, including measures of speech assessment. This study evaluates the type and timing of speech outcome measures currently included in this ICHOM Standard Set. Additionally, speech assessments in other cleft protocols and initiatives are discussed. Design, setting and participants An international, multicentre study was set up including centres from the USA and the Netherlands. Outcomes of clinical measures and Patient Reported Outcome Measures (PROMs) were collected retrospectively according to the ICHOM set. PROM data from a field test of the CLEFT-Q, a questionnaire developed and validated for patients with a cleft, were collected, including participants from countries with all sorts of income statuses, to examine the value of additional moments of measurement that are used in other cleft initiatives. Data from 2500 patients were included. Measured outcomes contained univariate regression analyses, trend analyses, t-tests, correlations and floor and ceiling effects. Results PROMs correlated low to moderate with clinical outcome measures. Clinical outcome measures correlated low to moderate with each other too. In contrast, two CLEFT-Q Scales correlated strongly with each other. All PROMs and the Percent Consonants Correct (PCC) showed an effect of age. In patients with an isolated cleft palate, a ceiling effect was found in the Intelligibility in Context Scale. Conclusion Recommendations for an optimal speech outcome assessment in cleft patients are made. Measurement moments of different cleft protocols and initiatives are considered in this proposition. Concerning the type of measures, adjustment of the current PCC score outcome seems appropriate. For centres with adequate resources and specific interest in research, translation and validation of an upcoming tool, the Cleft Audit Protocol for Speech Augmented , is recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".