Predictors for the prescription of pharmacological prophylaxis for venous thromboembolism during hospitalization in Internal Medicine: a sub-analysis of the FADOI-NoTEVole study
Bibliographic record
Abstract
Patients hospitalized in Internal Medicine Units (IMUs) may frequently experience both an increased risk for thrombosis and bleeding. The use of risk assessment models (RAMs) could aid their management. We present a post-hoc analysis of the FADOI-NoTEVole study, an observational, retrospective, multi-center study conducted in 38 Italian IMUs. The primary aim of the study was to evaluate the predictors associated with the prescription of thromboprophylaxis during hospitalization. The secondary objective was to evaluate RAMs adherence. Univariate analyses were conducted as preliminary evaluations of the variables associated with prescribing pharmacological thromboprophylaxis during hospital stay. The final multivariable logistic model was obtained by a stepwise selection method, using 0.05 as the significance level for entering an effect into the model. Thromboprophylaxis was then correlated with the RAMs and the number of predictors found in the multivariate analysis. Thromboprophylaxis was prescribed to 927 out of 1387 (66.8%) patients with a Padua Prediction score (PPS) ≥ 4. Remarkably, 397 in 1230 (32.3%) patients with both PPS ≥ 4 and an IMPROVE bleeding risk score (IBS) < 7 did not receive it. The prescription of thromboprophylaxis mostly correlated with reduced mobility (OR 2.31; 95% CI 1.90-2.81), ischemic stroke (OR 2.38; 95% CI 1.34-2.91), history of previous thrombosis (OR 2.46; 95% CI 1.49-4.07), and the presence of a central venous catheter (OR 3.00; 95% CI 1.99-4.54). The bleeding risk assessment using the IBS did not appear to impact physicians' decisions. Our analysis provides insight into how indications for thromboprophylaxis were determined, highlighting the difficulties faced by physicians with patients admitted to IMUs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".