PerIoperative iNflammatory reSponse assessment In hiGH-risk patienTs undergoing non-cardiac surgery (INSIGHT): study protocol of a prospective non-interventional observational study
Bibliographic record
Abstract
Introduction Increased inflammatory processes after non-cardiac surgery are very common. The association between postoperative inflammation and the occurrence of cardiovascular complications after non-cardiac surgery are still not entirely clear. Therefore, we will evaluate the association between postoperative inflammation and the occurrence of major cardiovascular complications in patients at-risk for cardiovascular complications undergoing non-cardiac surgery. We will further evaluate the association of postoperative inflammation and days-at-home within 30 days after surgery (DAH 30 ), the incidence of acute kidney injury, postoperative N-terminal probrain natriuretic peptide (NT-proBNP) concentrations and neurocognitive decline. Methods and analysis In this multicentre study, we will include 1400 patients at-risk for cardiovascular complications undergoing non-cardiac surgery. Our primary aim is to evaluate the association of postoperative maximum C-reactive protein concentration and the occurrence of a composite of five major cardiovascular complications (myocardial infarction, myocardial injury after non-cardiac surgery, new onset of atrial fibrillation, stroke and death) within 30 days after surgery using a Mann-Whitney-U test as well as a logistic regression model. As our secondary aim, we will evaluate the association of a composite of three inflammatory biomarkers (interleukin 6, procalcitonin and copeptin) on the occurrence of our composite of five cardiovascular complications within 30 days and 1 year after surgery, acute kidney injury, DAH 30 and NT-proBNP concentrations using linear or logistic regression models. We will measure inflammatory biomarkers before surgery, and on the first, second, third and fifth postoperative day. We will check medical records and conduct a telephone survey 30 days and 1 year after surgery. We evaluate neurocognitive function, using a Montreal Cognitive Assessment, before and 1 year after surgery. Ethics and dissemination This study was approved by the ethics committees at the Medical University of Vienna (2458/2020) and at the Medical University of Graz (33-274 ex 20/21). Trial registration number NCT04753307 .
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".