Sustainable Cleft Care: A Comprehensive Model Based on the Global Smile Foundation Experience
Bibliographic record
Abstract
INTRODUCTION: Clefts of the lip and palate are leading congenital facial anomalies. Underserved patients with these facial differences lack access to medical care, surgical expertise, prenatal care, or psychological support. Moreover, the disease results in significant economic strains on patients and their families. While surgical outreach programs have attempted to fill this void, significant challenges facing international comprehensive cleft care persist. OBJECTIVE: Propose a path toward international sustainable cleft care based on the Global Smile Foundation experience. RESULTS: International sustainable comprehensive cleft care can be achieved by regulating surgical outreach programs. Regulation of these missions would ensure standardized care and encourage stakeholders to cooperate and adequately allocate funding and resources. Capacity building can be achieved through "diagonal" cleft care delivery models, multidisciplinary workshops, fellowship programs, research and quality assurance, as well as leveraging emerging technologies such as Augmented Reality. CONCLUSION: International comprehensive cleft care requires continuous collaborative efforts between visiting and local teams as well as international and national organizations. Standardizing and regulating current practices as well as promoting capacity building initiatives can contribute to sustainable cleft care.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".