P1623Association of low hemoglobin with venous thromboembolism in acutely ill hospitalized medical patients: findings from the APEX trial
Bibliographic record
Abstract
Background: Anemia is a common finding and independent predictor for adverse outcomes in hospitalized patients with medical illness. It remains unclear whether anemia is a risk factor for venous thromboembolism (VTE) and whether the presence of anemia can refine risk assessment for prediction of VTE, thereby adding incremental utility to a validated VTE model. Methods: In the APEX trial, 7,513 hospitalized medical patients were randomized to receive either betrixaban or standard-of-care enoxaparin for thromboprophylaxis. Baseline hemoglobin concentrations were obtained in 6,861 patients with a follow-up of 77 days. Symptomatic VTE events, including symptomatic deep vein thrombosis (DVT), pulmonary embolism (PE), and VTE-related mortality, were compared between low hemoglobin and normal hemoglobin group (normal range: 12.5 to 17.0 g/dL for males and 11.0 to 15.5 g/dL for females). The relationship between anemia and VTE events was assessed by fitting a univariable and multivariable logistic regression model composed of thromboprophylaxis and VTE risk factors. VTE risk refinement by hemoglobin measurement was evaluated in the IMPROVE risk assessment model.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".