Risk-assessment models for VTE and bleeding in hospitalized medical patients: an overview of systematic reviews
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| 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 it