Deriving literature-based benchmarks for surgical complications in high-income countries: a protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: To improve surgical safety, health systems must identify preventable adverse outcomes and measure changes in these outcomes in response to quality improvement initiatives. This requires understanding of the scope and limitations of available population-level data. To derive literature-based summary estimates of benchmarks of care, we will systematically review and meta-analyse rates of postoperative complications associated with several common and/or high-risk operations performed in five high-income countries (HICs). METHODS AND ANALYSIS: An electronic search of PubMed, Embase, Web of Science, Cochrane Central, the NHS Economic Evaluations Database and Health Technology Assessment database will be performed to identify studies reviewing national surgical complication rates between 2000 and 2016. Two reviewers will screen titles and abstracts and full texts of potentially relevant studies to determine eligibility for inclusion in the systematic review. We will include English-language publications using data from health databases in the USA, Canada, the UK, Australia and New Zealand. We will include studies of patients who underwent hip or knee arthoplasty, appendectomy, cholecystectomy, oesophagectomy, abdominal aortic aneurysm repair, aortic valve replacement or coronary artery bypass graft. Outcomes will include mortality, length of hospital stay, pulmonary embolism, pneumonia, sepsis or septic shock, reoperation, surgical site infection, wound dehiscence/disruption, blood transfusion, bile duct injury, stroke and myocardial infarction. We will calculate summary estimates of cumulative incidence, incidence rate, prevalence and occurrence rate of complications using DerSimonian and Laird random effects models. Heterogeneity in these estimates will be examined using subgroup analyses and meta-regression. We will correlate findings within contemporary clinical databases. ETHICS AND DISSEMINATION: This study of secondary data does not require ethics approval. It will be presented internationally and published in the peer-reviewed literature. Results will inform a future quality improvement tool and provide benchmarks of surgical complication rates within HICs. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO). Registration number CRD42016037519.
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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 | 0.141 | 0.227 |
| Meta-epidemiology (narrow) | 0.009 | 0.007 |
| Meta-epidemiology (broad) | 0.026 | 0.042 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".