CLEFT-Q: Detecting Differences in Outcomes among 2434 Patients with Varying Cleft Types
Bibliographic record
Abstract
BACKGROUND: Measuring the patient perspective is important in evaluating outcomes of cleft care. Understanding how treatment outcomes vary depending on cleft type may allow for better planning of treatments, setting of expectations, and more accurate benchmarking efforts. The CLEFT-Q is a patient-reported outcome measure for patients with cleft lip and/or palate. METHODS: The 12 CLEFT-Q scales measuring appearance (i.e., face, nose, nostrils, lips, cleft lip scar, teeth, and jaws), function (i.e., speech), and health-related quality of life (i.e., psychological, school, social, and speech-related distress) were field tested in a cross-sectional study in 30 centers in 12 countries. Patients with cleft lip and/or cleft palate aged 8 to 29 years were recruited from clinical settings. Differences in CLEFT-Q scores by cleft subtypes were evaluated using one-way analysis of variance or Kruskal-Wallis H tests, with Tukey or Dunn procedure with Bonferroni corrections post hoc analyses, respectively. Scores are presented using radar charts to visualize all outcomes simultaneously. RESULTS: The field test included 2434 patients. Scores on all CLEFT-Q scales varied significantly with cleft subtype. Patients with unilateral or bilateral cleft lip and/or palate scored lower on all appearance scales compared with patients with cleft palate or unilateral incomplete cleft lip. Scores on the speech function and speech-related distress scales decreased with each progressive group in the Veau classification. Patients with complete bilateral cleft lip and palate scored lowest on the social, school, and psychological scales. CONCLUSIONS: Patient-reported outcomes measured with the CLEFT-Q vary significantly with cleft type. Visualizing multiple outcomes simultaneously with radar charts allows for an understanding of a patient's overall status in a single graph.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".