Defining Feasibility as a Criterion for Essential Surgery: A Qualitative Study with Global Children's Surgery Experts
Bibliographic record
Abstract
BACKGROUND: The Disease Control Priorities (DCP-3) group defines surgery as essential if it addresses a significant burden, is cost-effective, and is feasible-yet the feasibility component remains largely unexplored. The aim of this study was to develop a precise definition of feasibility for essential surgical procedures for children. METHODS: Four online focus group discussions (FGDs) were organized among 19 global children's surgery providers with experience of working in low- and lower-middle-income countries (LMICs), representing 10 countries. FGDs were transcribed verbatim, and qualitative data analysis was performed. Codes, categories, themes, and subthemes were identified. RESULTS: Six determinants of feasibility were identified, including: adequate human resources; adequate material resources; procedure and disease complexity; team commitment and understanding of their setting; timely access to care; and the ability to monitor and achieve good outcomes. Factors unique to feasibility of children's surgery included children's right to health and their reliance on adults for accessing safe and timely care; the need for specialist workforce; and children's unique perioperative care needs. FGD participants reported a greater need for task-sharing and shifting, creativity, and adaptability in resource-limited settings. Resource availability was seen to have a direct impact on decision-making and prioritization, e.g., saving a life versus achieving the best outcome. CONCLUSIONS: The identification of a precise definition of feasibility serves as a pivotal step in identifying a list of essential surgical procedures for children, which would serve as indicators of institutional surgical capacity for this age group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".