P620Association of anemia with stroke/systemic embolism, bleeding, and cardiovascular death in patients with atrial fibrillation: The Fushimi AF Registry
Bibliographic record
Abstract
Background: Anemia often affects the incidence of cardiovascular (CV) events and, particularly in patients with atrial fibrillation (AF), is also associated with the clinical decision-making in the management of anticoagulation therapy. However, data regarding the CV events in anemic AF patients remain scarce. Purpose: To examine the differences in the clinical characteristics and incidences of stroke/systemic embolism (SE), major bleeding, and CV death in AF patients, depending on the severity of anemia. Methods: The Fushimi AF Registry is a community-based prospective survey of AF patients in Fushimi-ku, Kyoto. The inclusion criterion for the registry is the documentation of AF at 12-lead electrocardiogram or Holter monitoring at any time. We started to enroll patients from March 2011, and baseline characteristics including hemoglobin level and follow-up data were available for 3,804 patients by the end of November 2016. Anemia was categorized into three levels depending on the hemoglobin value; mild (10.0 to <13.0 g/dl for men and <12.0 g/dl for women), moderate (7.0 to <10.0 g/dl), and severe (<7.0 g/dl). The study population was divided into the 3 groups: non-anemic (Group 1: 2,404 patients), mild anemic (Group 2: 1,071 patients), and moderate/severe anemic patients (Group 3: 329 patients).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".