The Global Initiative for Children's Surgery: Optimal Resources for Improving Care
Bibliographic record
Abstract
BACKGROUND: (Debas HTP, Donkor A, Gawande DT, Jamison ME, Kruk, and Mock CN, editors. Essential Surgery. Disease Control Priorities. Third Edition, vol 1. Essential Surgery. Washington, DC: World Bank; 2015) on surgery included guidelines for the improvement of access to surgical care; however, these lack detail for children's surgery. AIM: To produce guidance for low- and middle-income countries (LMICs) on the resources required for children's surgery at each level of hospital care. METHODS: The Global Initiative for Children's Surgery (GICS) held an inaugural meeting at the Royal College of Surgeons in London in May 2016, with 52 surgical providers from 21 countries, including 27 providers from 18 LMICs. Delegates engaged in working groups over 2 days to prioritize needs and solutions for optimizing children's surgical care; these were categorized into infrastructure, service delivery, training, and research. At a second GICS meeting in Washington in October 2016, 94 surgical care providers, half from LMICs, defined the optimal resources required at primary, secondary, tertiary, and national referral level through a series of working group engagements. RESULTS: Consensus solutions for optimizing children's surgical care included the following: · Establishing standards and integrating them into national surgical plans.. · Each country should have at least one children's hospital.. · Designate, facilitate, and support regional training hubs covering all. · children's surgical specialties.. · Establish regional research support centers.. An "Optimal Resources" document was produced detailing the facilities and resources required at each level of care. CONCLUSION: The Optimal Resources document has been produced by surgical providers from LMICs who have the greatest insight into the needs and priorities in their population. The document will be refined further through online GICS Working Groups and the World Health Organization for broad application to ensure all children have timely access to safe surgical 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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".