External validation of a risk assessment model for venous thromboembolism in the hospitalised acutely-ill medical patient (VTE-VALOURR)
Bibliographic record
Abstract
Venous thromboembolic (VTE) risk assessment remains an important issue in hospitalised, acutely-ill medical patients, and several VTE risk assessment models (RAM) have been proposed. The purpose of this large retrospective cohort study was to externally validate the IMPROVE RAM using a large database of three acute care hospitals. We studied 41,486 hospitalisations (28,744 unique patients) with 1,240 VTE hospitalisations (1,135 unique patients) in the VTE cohort and 40,246 VTE-free hospitalisations (27,609 unique patients) in the control cohort. After chart review, 139 unique VTE patients were identified and 278 randomly-selected matched patients in the control cohort. Seven independent VTE risk factors as part of the RAM in the derivation cohort were identified. In the validation cohort, the incidence of VTE was 0.20%; 95% confidence interval (CI) 0.18-0.22, 1.04%; 95%CI 0.88-1.25, and 4.15%; 95%CI 2.79-8.12 in the low, moderate, and high VTE risk groups, respectively, which compared to rates of 0.45%, 1.3%, and 4.74% in the three risk categories of the derivation cohort. For the derivation and validation cohorts, the total percentage of patients in low, moderate and high VTE risk occurred in 68.6% vs 63.3%, 24.8% vs 31.1%, and 6.5% vs 5.5%, respectively. Overall, the area under the receiver-operator characteristics curve for the validation cohort was 0.7731. In conclusion, the IMPROVE RAM can accurately identify medical patients at low, moderate, and high VTE risk. This will tailor future thromboprophylactic strategies in this population as well as identify particularly high VTE risk patients in whom multimodal or more intensive prophylaxis may be beneficial.
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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 | 0.000 |
| 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".