Risk models for VTE and bleeding in medical inpatients: systematic identification and expert assessment
Bibliographic record
Abstract
Risk assessment models (RAMs) for venous thromboembolism (VTE) and bleeding in hospitalized medical patients inform appropriate use of thromboprophylaxis. Our aim was to use a novel approach for selecting risk factors for VTE and bleeding to be included in RAMs. First, we used the results of a systematic review of all candidate factors. Second, we used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of the evidence for the identified factors. Third, we using a structured approach to select factors to develop the RAMs, by building on clinical and methodological expertise. The expert panel made judgments on whether to include, potentially include, or exclude risk factors, according to domains of the GRADE approach and the Delphi method. The VTE RAM included age >60 years, previous VTE, acute infections, immobility, acute paresis, active malignancy, critical illness, and known thrombophilia. The bleeding RAM included age ≥65 years, renal failure, thrombocytopenia, active gastroduodenal ulcers, hepatic disease, recent bleeding, and critical illness. We identified acute infection as a factor that was not considered in widely used RAMs. Also, we identified factors that require further research to confirm or refute their importance in a VTE RAM (eg, D-dimer). We excluded autoimmune disease which is included in the IMPROVE (International Medical Prevention Registry on Venous Thromboembolism) bleeding RAM. Our results also suggest that sex, malignancy, and use of central venous catheters (factors in the IMPROVE bleeding RAM) require further research. In conclusion, our study presents a novel approach to systematically identifying and assessing risk factors to be included or further explored during RAM development.
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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 | 0.223 | 0.392 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.023 |
| Bibliometrics | 0.021 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".