Interventions to reduce short term post-operative mortality in Low- and Middle-Income Countries: A Protocol for a Systematic Review of randomised controlled trials.
Bibliographic record
Abstract
Introduction: Post-operative mortality is the third leading cause of death worldwide. Patients in low- and middle-income countries (LMIC) are at disproportionately increased risk, and it is likely that many deaths are avoidable and preventable. We aim to consolidate evidence on interventions tested in a randomised setting to reduce short term post-operative mortality following non-cardiac surgery in LMICs. Methods: We will conduct a systematic review of all randomised controlled trials of interventions aimed at reducing short term post-operative mortality in Low- and Middle-Income countries. Short term mortality is defined as within 90 days of surgery. Trials will be included if they pertain to non-cardiac surgery in both elective and emergency settings and report mortality as their primary outcome. If trials are conducted across multiple countries, we will include them if we are able to extract the LMIC data separately. Our primary aim is to consolidate evidence on interventions tested in a randomised setting to reduce short term post-operative mortality. We will achieve this by several secondary outcomes which are to identify the number of RCTs of interventions tested in this context; describe the interventions, their components, and their timing on the patient pathway; the geographical location the trials were conducted and the adherence to the interventions. Finally, we will describe the impact on mortality of the interventions. Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Data on individual components of each intervention will be collected and thematically coded and grouped. We will use these groupings to map each intervention across the surgical pathway from the pre-operative phase and decision for surgery to postoperative rehabilitation. Formal ethical approval is not required as primary data will not be collected. Discussion: Despite reductions in perioperative mortality globally in the last 50 years, post-operative mortality is the third leading cause of death, with 4.2 million people dying within 30 days of an operation each year. This systematic review will identify and discuss interventions tested in a randomised setting to reduce short-term post-operative mortality following non-cardiac surgery in LMICs. This will inform future trial design by identifying successful interventions or knowledge gaps in the patient pathway in which interventions have not been tested. Prospero registration number: CRD42024604760
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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 | 0.163 | 0.193 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.024 | 0.027 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.044 | 0.011 |
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".