Risk assessment of venous thromboembolism in hospitalized medical patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim is to provide a concise review of risk assessment models that stratify hospitalized acutely ill medical patients at risk of venous thromboembolism (VTE). RECENT FINDINGS: Risk-assessment models (RAMs) for hospitalized medical patients at risk for VTE prior to 2005 attempted to identify at-risk patients using a point system or binary yes/no approach as to the existence of exposing (acute medical illness) or predisposing (genetic or clinical characteristic) risk factors for VTE. These RAMs were derived from data predominately from patient subgroups within randomized controlled trials and were cumbersome, not subject to rigorous validation, and were based on limited evidence of how these risk factors interacted in a quantitative manner. Recently, simplified RAMs have been proposed that have included this patient group. The RAMs are composed of various point systems and a threshold, which then would identify at-risk patient groups that would benefit from thromboprophylaxis. Although some of the point systems have been derived intuitively, they have been validated in large patient cohorts either prospectively or retrospectively and have shown good sensitivity. The presence of malignancy, prior VTE, hypercoagulability, advanced age and immobility all conferred increased risk of VTE during hospitalization or in the posthospital discharge period in the various models. SUMMARY: Simple RAMs based on point systems to predict risk of VTE for the hospitalized medical patient have been validated that include either exposing or predisposing risk factors for VTE. It is hoped that these RAMs may identify acutely ill medical patients with additional characteristics that do not easily fit into group-specific thromboembolic risk assessment categories as currently proposed by international clinical guidelines.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".