Bleeding impacting mortality after noncardiac surgery: a protocol to establish diagnostic criteria, estimate prognostic importance, and develop and validate a prediction guide in an international prospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Various definitions of bleeding have been used in perioperative studies without systematic assessment of the diagnostic criteria for their independent association with outcomes important to patients. Our proposed definition of bleeding impacting mortality after noncardiac surgery (BIMS) is bleeding that is independently associated with death during or within 30 days after noncardiac surgery. We describe our analysis plan to sequentially 1) establish the diagnostic criteria for BIMS, 2) estimate the independent contribution of BIMS to 30-day mortality and 3) develop and internally validate a clinical prediction guide to estimate patient-specific risk of BIMS. METHODS: In the Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) study, we prospectively collected bleeding data for 16 079 patients aged 45 years or more who had noncardiac inpatient surgery between 2007 and 2011 at 12 centres in 8 countries across 5 continents. We will include bleeding features independently associated with 30-day mortality in the diagnostic criteria for BIMS. Candidate features will include the need for reoperation due to bleeding, the number of units of erythrocytes transfused, the lowest postoperative hemoglobin concentration, and the absolute and relative decrements in hemoglobin concentration from the preoperative value. We will then estimate the incidence of BIMS and its independent association with 30-day mortality. Last, we will construct and internally validate a clinical prediction guide for BIMS. INTERPRETATION: ClinicalTrials.gov, no NCT00512109.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".