Universal access to safe, affordable, timely surgical and anaesthetic care in Papua New Guinea: the six global health indicators
Bibliographic record
Abstract
BACKGROUND: The unmet global burden of surgical disease is substantial. The Lancet Commission on Global Surgery (LCoGS) estimated that 5 billion people do not have access to safe, affordable and timely surgical care, with 80% of those without access living in low- and middle-income countries. The Milne Bay Province (pop 331 000) of Papua New Guinea, with an archipelago of islands up to 750 km from its capital, Alotau, has only one hospital capable of performing Caesarean Section, Emergency Laparotomy and managing an open fracture, the three Bellwether procedures. This paper aims to report the six Lancet Commission on Global Surgery metrics for Milne Bay Province. METHODS: The study was conducted between January and August 2019. Bellwether access was investigated by a prospective study on 115 patients presenting to hospital. The surgical, anaesthesia and obstetric (SAO) workforce, surgical volume and perioperative mortality rate, were calculated for 2012-2018 from hospital records and operation registers. Financial risk metrics were calculated by surveying 50 patients at discharge from hospital. RESULTS: Bellwether access: Only 27.8% (n = 32) of the study population (n = 115) experienced less than 2-hours second delay (journey time to hospital). The average SAO provider density was 1.8 per 100 000 population. There were 606 procedures performed per 100 000 with a mean annual perioperative mortality rate of 0.3%. Catastrophic expenditure is a risk for 29% of the population. CONCLUSION: Milne Bay Province can perform surgery safely, but there is limited access to timely surgical care when needed with a significant proportion put at financial risk by requiring it.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".