Risk-assessment models for VTE and bleeding in hospitalized medical patients: an overview of systematic reviews
Bibliographic record
Abstract
Multiple risk-assessment models (RAMs) for venous thromboembolism (VTE) in hospitalized medical patients have been developed. To inform the 2018 American Society of Hematology (ASH) guidelines on VTE, we conducted an overview of systematic reviews to identify and summarize evidence related to RAMs for VTE and bleeding in medical inpatients. We searched Epistemonikos, the Cochrane Database, Medline, and Embase from 2005 through June 2017 and then updated the search in January 2020 to identify systematic reviews that included RAMs for VTE and bleeding in medical inpatients. We conducted study selection, data abstraction and quality assessment (using the Risk of Bias in Systematic Reviews [ROBIS] tool) independently and in duplicate. We described the characteristics of the reviews and their included studies, and compared the identified RAMs using narrative synthesis. Of 15 348 citations, we included 2 systematic reviews, of which 1 had low risk of bias. The reviews included 19 unique studies reporting on 15 RAMs. Seven of the RAMs were derived using individual patient data in which risk factors were included based on their predictive ability in a regression analysis. The other 8 RAMs were empirically developed using consensus approaches, risk factors identified from a literature review, and clinical expertise. The RAMs that have been externally validated include the Caprini, Geneva, IMPROVE, Kucher, and Padua RAMs. The Padua, Geneva, and Kucher RAMs have been evaluated in impact studies that reported an increase in appropriate VTE prophylaxis rates. Our findings informed the ASH guidelines. They also aim to guide health care practitioners in their decision-making processes regarding appropriate individual prophylactic management.
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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 | 0.049 | 0.202 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".