The Risk of Major Hemorrhage with CKD
Bibliographic record
Abstract
New staging systems for CKD account for both reduced eGFR and albuminuria; whether each measure associates with greater risk of hemorrhage is unclear. In this retrospective cohort study (2002-2010), we grouped 516,197 adults ≥40 years old by eGFR (≥90, 60 to <90, 45 to <60, 30 to <45, 15 to <30, or <15 ml/min per 1.73 m(2)) and urine albumin-to-creatinine ratio (ACR; >300, 30-300, or <30 mg/g) to examine incidence of hemorrhage. The 3-year cumulative incidence of hemorrhage increased 20-fold across declining eGFR and increasing urine ACR groupings (highest eGFR/lowest ACR: 0.5%; lowest eGFR/highest ACR: 10.1%). Urine ACR altered the association of eGFR with hemorrhage (P<0.001). In adjusted models using the highest eGFR/lowest ACR grouping as the referent, patients with eGFR=15 to <30 ml/min per 1.73 m(2) had adjusted relative risks of hemorrhage of 1.9 (95% confidence interval [95% CI], 1.5 to 2.4) with the lowest ACR and 3.7 (95% CI, 3.0 to 4.5) with the highest ACR. Patients with the highest eGFR/highest ACR had an adjusted relative risk of hemorrhage of 2.3 (95% CI, 1.8 to 2.9), comparable with the risk for patients with the lowest eGFR/lowest ACR. The associations attenuated but remained significant after adjustment for anticoagulant and antiplatelet use in patients ≥66 years old. The risk of hemorrhage differed by urine ACR in high risk subgroups. Our data show that declining eGFR and increasing albuminuria each independently increase hemorrhage risk. Strategies to reduce hemorrhage events among patients with CKD are warranted.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".