Global Initiative for Children’s Surgery: A Model of Global Collaboration to Advance the Surgical Care of Children
Bibliographic record
Abstract
BACKGROUND: Recommendations by the Lancet Commission on Global Surgery regarding surgical care in low- and middle-income countries (LMICs) require development to address the needs of children. The Global Initiative for Children's Surgery (GICS) was founded in 2016 to identify solutions to problems in children's surgery by utilizing the expertise of practitioners from around the world. This report details this unique process and underlying principles. METHODS: Three global meetings convened providers of surgical services for children. Through working group meetings, participants reviewed the status of global children's surgery to develop priorities and identify necessary resources for implementation. Working groups were formed under LMIC leadership to address specific priorities. By creating networking opportunities, GICS has promoted the development of LMIC-LMIC and HIC-LMIC partnerships. RESULTS: GICS members identified priorities for children's surgical care within four pillars: infrastructure, service delivery, training and research. Guidelines for provision of care at every healthcare level based on these pillars were created. Seventeen subspecialty, LMIC chaired working groups developed the Optimal Resources for Children's Surgery (OReCS) document. The guidelines are stratified by subspecialty and level of health care: primary health center, first-, second- and third-level hospitals, and the national children's hospital. The OReCS document delineates the personnel, equipment, facilities, procedures, training, research and quality improvement components at all levels of care. CONCLUSION: Worldwide collaboration with leadership by providers from LMICs holds the promise of improving children's surgical care. GICS will continue to evolve in order to achieve the vision of safe, affordable, timely surgical care for all children.
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 | 0.037 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.035 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".