Cross-sectional study of surgical quality with a novel evidence-based tool for low-resource settings
Bibliographic record
Abstract
BACKGROUND: Adverse events from surgical care are a major cause of death and disability, particularly in low-and-middle-income countries. Metrics for quality of surgical care developed in high-income settings are resource-intensive and inappropriate in most lower resource settings. The purpose of this study was to apply and assess the feasibility of a new tool to measure surgical quality in resource-constrained settings. METHODS: This is a cross-sectional study of surgical quality using a novel evidence-based tool for quality measurement in low-resource settings. The tool was adapted for use at a tertiary hospital in Amazonas, Brazil resulting in 14 metrics of quality of care. Nine metrics were collected prospectively during a 4-week period, while five were collected retrospectively from the hospital administrative data and operating room logbooks. RESULTS: 183 surgeries were observed, 125 patient questionnaires were administered and patient charts for 1 year were reviewed. All metrics were successfully collected. The study site met the proposed targets for timely process (7 hours from admission to surgery) and effective outcome (3% readmission rate). Other indicators results were equitable structure (1.1 median patient income to catchment population) and equitable outcome (2.5% at risk of catastrophic expenditure), safe outcome (2.6% perioperative mortality rate) and effective structure (fully qualified surgeon present 98% of cases). CONCLUSION: It is feasible to apply a novel surgical quality measurement tool in resource-limited settings. Prospective collection of all metrics integrated within existing hospital structures is recommended. Further applications of the tool will allow the metrics and targets to be refined and weighted to better guide surgical quality improvement measures.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".