What Matters to Patients With Cleft Lip and/or Palate
Bibliographic record
Abstract
OBJECTIVE: The goal of treatment for individuals with cleft lip and/or palate (CL/P) is to improve physical, psychological, and social health. Outcomes of treatment are rarely measured from the patient's perspective. The aim of the study was to develop a conceptual framework for a patient-reported outcome (PRO) instrument for individuals with clefts (CLEFT-Q) by developing an in-depth understanding of issues that individuals consider to be important. DESIGN: The qualitative methodology of interpretive description was used. Setting, Participants, and Intervention: We performed 136 individual in-depth interviews with participants with clefts of any age, presenting for cleft care, across 6 countries. Parents were involved if the child was more comfortable. Interviews were audio-recorded, transcribed verbatim, and coded using constant comparison. The data were used to develop a refined conceptual framework. RESULTS: Participants described concepts of interest in 3 top-level domains, each of which included subdomains: appearance (face, nose, nostrils, teeth, lips, jaw, cleft lip scar), health-related quality of life (psychological, social, school, speech-related distress), and facial function (speech, eating/drinking). Participants were able to describe changes over time with regard to the 3 domains. CONCLUSIONS: A conceptual framework of concepts of interest to individuals with CL/P formed the basis of the scales in the CLEFT-Q. Each subdomain represents an independently functioning scale. Understanding what matters to patients is essential in guiding PRO measurement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".