Towards high‐quality peri‐operative care: a global perspective
Bibliographic record
Abstract
Article 25 of the United Nations' Universal Declaration of Human Rights enshrines the right to health and well-being for every individual. However, universal access to high-quality healthcare remains the purview of a handful of wealthy nations. This is no more apparent than in peri-operative care, where an estimated five billion individuals lack access to safe, affordable and timely surgical care. Delivery of surgery and anaesthesia in low-resource environments presents unique challenges that, when unaddressed, result in limited access to low-quality care. Current peri-operative research and clinical guidance often fail to acknowledge these system-level deficits and therefore have limited applicability in low-resource settings. In this manuscript, the authors priority-set the need for equitable access to high-quality peri-operative care and analyse the system-level contributors to excess peri-operative mortality rates, a key marker of quality of care. To provide examples of how research and investment may close the equity gap, a modified Delphi method was adopted to curate and appraise interventions which may, with subsequent research and evaluation, begin to address the barriers to high-quality peri-operative care in low- and middle-income countries.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".