A Prospective Study to Examine Responsiveness and Minimally Important Differences (MIDs) for the CLEFT-Q Scales Following Three Cleft-Specific Operations
Bibliographic record
Abstract
ObjectiveThe aim of this study was to examine internal responsiveness and estimate minimally important differences (MIDs) for CLEFT-Q scales.DesignIn this prospective cohort study, participants completed the CLEFT-Q appearance and health-related quality of life (HRQL) scales before and six months after cleft-related surgery.SettingSeven cleft centres in Canada, USA and UK participated.Patients/ParticipantsPatients were ages 8–29 years with CL/P.InterventionsPatients underwent rhinoplasty, orthognathic or cleft lip scar revision surgery.Main Outcome Measure(s)Internal responsiveness was examined using Cohen's d effect sizes (ESs) based on the following interpretation: 0.20–0.49 small, 0.50–0.79 moderate and ≥ 0.80 large. MIDs were estimated using two distribution-based approaches.ResultsParticipants had a rhinoplasty (n = 31), orthognathic (n = 21) or cleft lip scar revision (n = 18) surgery. Most participants were males (56%) and aged 8–11 years (41%). Following rhinoplasty, ESs were larger for the nose (0.92, p = 0.001) and nostrils (0.94, p ConclusionsCLEFT-Q detected change in key outcomes for three cleft-specific surgeries, providing evidence of its responsiveness. Estimated MIDs will aid in interpreting this PROM.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".