Innovate Global Plastic and Reconstructive Surgery: Cleft Lip and Palate Charity Database
Bibliographic record
Abstract
BACKGROUND: There is an emerging interest in global surgery. The Lancet Commission on Global Surgery recognizes the important role that nongovernmental organizations (NGOs) play in the delivery of cleft lip and/or palate (CLP) surgical care. To better address the unmet burden of surgical disease, the commissioners propose the use of a centralized registry to maximize coordination of global surgical volunteerism efforts. This study aims to create a comprehensive database of CLP organizations. METHODS: A systematic search of the following resources was conducted: The Plastic Surgery Foundation, Smile Train, Wikipedia, Google, and lists of surgical NGOs. A secondary review of each organization's website was performed to verify inclusion criteria and to extract data. Organizations were classified as providing surgical or nonsurgical care. RESULTS: Thirty-one organizations providing CLP care were reviewed, with 30 that met inclusion criteria. Of the 20 surgical NGOs, 50% use a diagonal approach of international outreach, 40% a vertical one-way approach, and 10% a horizontal approach. All 10 of the nonsurgical NGOs provide care through a horizontal approach. Their offices are distributed across North America (43%), Asia (27%), Europe (23%), and Australia (7%). Forty-three percent of the organizations provide CLP surgeries or services in more than 1 country; 93% do so with a multidisciplinary team. A majority of the organizations established collaborations with host institutions (80%). CONCLUSION: To the authors' best knowledge, this database includes the largest collection of CLP organizations. This list will be made publicly available to inform surgical care planning, facilitate collaboration, and promote further research.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".