Building resilient surgical systems that can withstand external shocks
Bibliographic record
Abstract
When surgical systems fail, there is the major collateral impact on patients, society and economies. While short-term impact on patient outcomes during periods of high system stress is easy to measure, the long-term repercussions of global crises are harder to quantify and require modelling studies with inherent uncertainty. When external stressors such as high-threat infectious disease, forced migration or climate-change-related events occur, there is a resulting surge in healthcare demand. This, directly and indirectly, affects perioperative pathways, increasing pressure on emergency, critical and operative care areas. While different stressors have different effects on healthcare systems, they share the common feature of exposing the weakest areas, at which point care pathways breakdown. Surgery has been identified as a highly vulnerable area for early failure. Despite efforts by the WHO to improve preparedness in the wake of the SARS-CoV-2 pandemic, measurement of healthcare investment and surgical preparedness metrics suggests that surgical care is not yet being prioritised by policy-makers. Investment in the 'response' phase of health system recovery without investment in the 'readiness' phase will not mitigate long-term health effects for patients as new stressors arise. This analysis aims to explore how surgical preparedness can be measured, identify emerging threats and explore their potential impact on surgical services. Finally, it aims to highlight the role of high-quality research in developing resilient surgical systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".