Psychosocial Outcomes in Children with Cleft Lip and/or Palate: Associations of Demographic, Cleft Morphologic, and Treatment-Related Variables
Bibliographic record
Abstract
OBJECTIVE: To determine associations of demographic, morphologic, and treatment protocol parameters with quality of life (QoL), appearance/speech satisfaction, and psychological adjustment. DESIGN: Observational study utilizing retrospective report of protocol variables and current outcome variables. SETTING: Six North American cleft treatment clinics. PARTICIPANTS: Children, ages 8.0-10.99 years, with Cleft Lip ± Alveolus, Cleft Palate, Cleft Lip and Palate, and parents (N = 284). OUTCOME MEASURES: Pediatric QoL Inventory (PedsQL): Parent, Child, Family Impact Module (FIM); Patient Reported Outcome Measurement Information System (PROMIS); Child Behavior Checklist (CBCL); CLEFT-Q. RESULTS: .05) between socioeconomic status, race, and age at assessment and parent- and self-reported measures. Females had higher PROMIS Depression (β=.20) but lower CBCL Affective (β = -.16) and PROMIS Stigma scores (β= -.24). Incomplete cleft lip was associated with lower PROMIS Depression, and more positive ratings of CLEFT-Q: Nose, Nostril, Lip Scar; CBCL Competence scores, (βs = -.17 to .17). Younger Age at Lip Closure was associated with higher CBCL School Competence (β= -.18). Younger Age at Palate Closure was associated with higher Child PedsQL Total, Physical, Psychosocial QoL, and better CLEFT-Q Speech Function (βs = -.18 to -.15). Furlow Palatoplasty was associated with more CBCL Externalizing Problems (β = .17) higher CBCL Activities (β = .16). For all diagnoses, fewer Total Cleft-Related Surgeries was associated with lower PROMIS Stigma and higher CBCL Total Competence and Activities (βs = -.16 to .15). CONCLUSIONS: Demographic characteristics, lip morphology, and treatment variables are related to later psychological functioning.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".