Psychometric findings and normative values for the CLEFT-Q based on 2434 children and young adult patients with cleft lip and/or palate from 12 countries
Bibliographic record
Abstract
BACKGROUND: Patients with cleft lip and/or palate can undergo numerous procedures to improve appearance, speech, dentition and hearing. We developed a cleft-specific patient-reported outcome instrument to facilitate rigorous international measurement and benchmarking. METHODS: Data were collected from patients aged 8-29 years with cleft lip and/or palate at 30 hospitals in 12 countries between October 2014 and November 2016. Rasch measurement theory analysis was used to refine the scales and to examine reliability and validity. Normative CLEFT-Q values were computed for age, sex and cleft type. RESULTS: Analysis led to the refinement of an eating and drinking checklist and 12 scales measuring appearance (of the face, nose, nostrils, teeth, lips, jaws and cleft lip scar), health-related quality of life (psychological, social, school, speech distress) and speech function. All scales met the requirements of the Rasch model. Analysis to explore differential item functioning by age, sex and country provided evidence to support the use of a common scoring algorithm for each scale for international use. Lower (worse) scores on CLEFT-Q scales were associated with having a speech problem, being unhappy with facial appearance, and needing future cleft-related treatments, providing evidence of construct validity. Normative values for age, sex and cleft type showed poorer outcomes associated with older age, female sex and having a visible cleft. INTERPRETATION: The CLEFT-Q represents a rigorously developed instrument that can be used internationally to collect and compare evidence-based outcomes data from patients aged 8-29 years of age with cleft lip and/or palate.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".