Establishing a baseline for surgical care in Mongolia: a situational analysis using the six indicators from the Lancet Commission on Global Surgery
Bibliographic record
Abstract
OBJECTIVES: Commission on Global Surgery were collected for the Mongolian surgical system. This situational analysis shows one lower middle-income country's ability to collect the indicators aided by a well-developed health information system. DESIGN: An 11-year retrospective analysis of the Mongolian surgical system using data from the Health Development Center, National Statistics Office and Household Socio-Economic Survey. Access estimates were based on travel time to capable hospitals. Provider density, surgical volume and postoperative mortality were calculated at national and regional levels. Protection against impoverishing and catastrophic expenditures was assessed against standard out-of-pocket expenditure at government hospitals for individual operations. SETTING: Mongolia's 81 public hospitals with surgical capability, including tertiary, secondary and primary/secondary facilities. PARTICIPANTS: All operative patients in Mongolia's public hospitals, 2006-2016. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes were national-level results of the indicators. Secondary outcomes include regional access; surgeons, anaesthesiologists and obstetricians (SAO) density; surgical volume; and perioperative mortality. RESULTS: In 2016, 80.1% of the population had 2-hour access to essential surgery, including 60% of those outside the capital. SAO density was 47.4/100 000 population. A coding change increased surgical volume to 5784/100 000 population, and in-hospital mortality decreased from 0.27% to 0.14%. All households were financially protected from caesarean section. Appendectomy carried 99.4% and 98.4% protection, external femur fixation carried 75.4% and 50.7% protection from impoverishing and catastrophic expenditures, respectively. Laparoscopic cholecystectomy carried 42.9% protection from both. CONCLUSIONS: Mongolia meets national benchmarks for access, provider density, surgical volume and postoperative mortality with notable limitations. Significant disparities exist between regions. Unequal access may be efficiently addressed by strengthening or building key district hospitals in population-dense areas. Increased financial protections are needed for operations involving hardware or technology. Ongoing monitoring and evaluation will support the development of context-specific interventions to improve surgical care in Mongolia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".