Expansion of national surgical, obstetric, and anaesthesia plans in Latin America: can Brazil be next?
Bibliographic record
Abstract
On the sidelines of the 75th Session of the Regional Committee of the World Health Organization for the Americas, the Republic of Ecuador hosted an event to expand on National Surgical, Obstetric, and Anaesthesia Plans (NSOAPs). NSOAPs are policy frameworks that offer governments a pathway to incorporate surgical planning into their overall health strategies. In Latin America, Ecuador became the first country to lead the development of an NSOAP and is fostering regional efforts for other Latin American countries to have sustainable surgical strengthening plans. Brazil is a prominent candidate for enrolling in an NSOAP process to enhance its public health system's functionality. An NSOAP in Brazil can help mitigate social disparities, promote greater efficiency in allocating existing resources, and optimise public health system financing. This process can also encourage the creation of resources and distinct NSOAP vocabulary in Portuguese to facilitate the development of NSOAPs in other Portuguese-speaking and low- and middle-income countries. In this viewpoint, we explore why an NSOAP can benefit Brazil's surgical system, national features that enable surgical policymaking, and how multiple stakeholder engagement can contribute to the country's planning, validation, and implementation of an NSOAP.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".