The Americleft Psychosocial Outcomes Project: A Multicenter Approach to Advancing Psychosocial Outcomes for Youth With Cleft Lip and Palate
Bibliographic record
Abstract
Cleft lip and/or palate (CL/P) are among the most common of all birth defects. Habilitation requires multiple surgeries and other therapies throughout childhood and adolescence. While multidisciplinary care is recommended, there is a great deal of variation in treatment protocols for this condition. There is ample evidence that children with CL/P are at risk for psychosocial problems. However, to date, few studies have systematically investigated parent and patient self-reported psychosocial and quality of life (QOL) outcomes for children with CL/P as they relate to variations in treatment protocols. The Americleft Outcomes project was initiated to demonstrate and document outcomes to be expected with team care, and to define the key features or characteristics of various team treatment protocols and procedures that are associated with more or less favorable/desirable outcomes. This article will describe the psychosocial component of the Americleft Outcomes project that is aimed at developing a protocol that will allow cross team assessment of psychosocial outcomes for children with CL/P in relationship to the treatments they received. The protocol will be detailed along with a description of the process and considerations that were instrumental in the development of the project. Stakeholder input about the project's perceived relevance to families of children with CL/P will be reported. The paper concludes with a discussion of the challenges encountered with this project, clinical implications, and future directions.
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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 | 0.054 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".