Rates of venous thromboembolism occurrence in medical patients among the insured population
Bibliographic record
Abstract
The burden of venous thromboembolism (VTE) remains high in the United States (US). This study assesses the rate of VTE prophylaxis in a large real-world population of medically ill patients and identifies factors which confer VTE risk to this population. Discharges from the PharMetrics database were included if they were aged > or =40 years and had a hospitalisation claim (Jan 2001-Dec 2005) for cancer, congestive heart failure (CHF), severe infectious disease (SID), or lung disease. Discharges with incomplete records in the prior year to the index hospitalisation claim date were excluded. VTE rate, type (deep venous thrombosis [DVT] or pulmonary embolism [PE]), and time to VTE were compared between groups. Multivariate logistic regression analysis was used to identify independent predictors of VTE occurrence. A total of 158,325 patients were included in the study. Cancer patients had the highest incidence of VTE (7.6%), with the average for all patients being 5.6% (1.5% PE). VTE occurred most frequently post discharge, with the median time being 74 days. Post-discharge prophylaxis was provided to 13.1% of CHF patients and < 5% of all other patients. Independent predictors of VTE included a pre-index VTE (odds ratio [OR] 9.06, 95% confidence interval [CI] 8.28-9.91) and a primary diagnosis of cancer compared with a diagnosis of SID (OR 1.34, 95% CI 1.24-1.46). In conclusion, commercially insured medical patients in the US are at high risk of VTE following hospital discharge. One-quarter of medical patients who developed a VTE are at high risk of developing the more severe form of the disease, namely PE, with independent predictors of VTE in the post-discharge period including previous VTE and cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".