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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.001 | 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; both teacher heads agree on what is shown here.
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".