Assessment of diagnostics capacity in hospitals providing surgical care in two Latin American states
Bibliographic record
Abstract
Background Diagnostic services are an essential component of high-quality surgical, anesthesia and obstetric (SAO) care. Efforts to scale up SAO care in Latin America have often overlooked diagnostics capacity. This study aims to analyze the capacity of diagnostic services, including radiology, pathology, and laboratory medicine, in hospitals providing SAO care in the states of Chiapas, Mexico and Amazonas, Brazil. Methods A stratified cross-sectional evaluation of diagnostic capacity in hospitals performing surgery in Chiapas and Amazonas was performed using the Surgical Assessment Tool (SAT). National data sources were queried for indicators of diagnostics capacity in terms of workforce, infrastructure and diagnosis utilization. Fisher's exact tests and chi-square tests were used to compare categorical variables between the private and public sector in Chiapas while descriptive statistics are used to compare Amazonas and Chiapas. Findings In Chiapas, 53% ( n = 17) of public and 34% ( n = 20) of private hospitals providing SAO care were assessed. More private hospitals than public hospitals could always provide x-rays (35% vs 23.5%) and ultrasound (85% vs 47.1%). However neither sector could consistently perform basic laboratory testing such as complete blood counts (70.6% public, 65% private). In Amazonas, 30% ( n = 18) of rural hospitals were surveyed. Most had functioning x-ray machine (77.8%) and ultrasound (55.6%). The majority of hospitals could provide complete blood count (66.7%) but only one hospital (5.6%) could always perform an infectious panel. Both Chiapas and Amazonas had dramatically fewer diagnostic practitioners per capita in each state compared to the national average capacity. Interpretation Facilities providing SAO care in low-resource states in Mexico and Brazil often lack functioning diagnostics services and workforce. Scale-up of diagnostic services is essential to improve SAO care and should occur with emphasis on equitable and adequate resource allocation.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".