Establishing Content Validity of the CLEFT-Q: A New Patient-reported Outcome Instrument for Cleft Lip/Palate
Bibliographic record
Abstract
BACKGROUND: The CLEFT-Q is a new patient-reported outcome instrument designed to measure outcomes that matter to patients. The aim of this qualitative study was to establish content validity of the CLEFT-Q in patients who differ by age and culture. METHODS: Patients aged between 6 and 29 years were recruited from plastic surgery clinics in Canada, India, Ireland, the Philippines, the Netherlands and the United States. Healthcare providers and other experts participated in a focus group or provided individual feedback. Input was sought on all aspects of the CLEFT-Q (item wording, instructions, and response options), and to identify missing content. Patient interviews and expert feedback took place between September 2013 and September 2014. RESULTS: Sixty-nine patients and 44 experts participated. The first draft of the CLEFT-Q consisted of 163 items measuring 12 constructs. The first round of feedback identified 92 items that required revision. In total, 3 rounds of interviews, and the involvement of an artist to create pictures for 17 items, were needed to establish content validity. At the conclusion of cognitive interviews, the CLEFT-Q consisted of 13 scales (total 171 items) that measure appearance, health-related quality of life, and facial function. The mean Flesch-Kincaid readability statistic for items was 1.4 (0 to 5.2). CONCLUSION: Cognitive interviews and expert review allowed us to identify items that required re-wording, re-conceptualizing, or to be removed, as well as any missing items. This process was useful for refining the CLEFT-Q scales for further testing.
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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 | 0.065 | 0.125 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".