Comparative evaluation of venous thromboembolic risk in urologic inpatients using different risk assessment models
Bibliographic record
Abstract
INTRODUCTIONS: The process for determining thromboprophylaxis decisions in urologic surgery entails assessing the risk of venous thromboembolism (VTE) in comparison to the risk of bleeding. Risk assessment models (RAMs) have been created to systematically calculate an individual's risk of VTE. In our study, we evaluated the risk of VTE in urologic inpatients using two RAMs specifically designed for urology by the European Association of Urology (EAU) and the American Urological Association (AUA), the Caprini score, and the CHA2DS2-VASc score. METHODS: The study group consisted of 136 inpatients within the urology department. Data from medical records included information on various factors, such as age, gender, and body mass index, as well as personal and family history of the patients. The risk of VTE was determined using the RAMs provided by EAU and AUA, the Caprini score, and the CHA2DS2-VASc score. RESULTS: Chemical prophylaxis was advised for 48 (35.3%) patients according to the EAU model, 47 patients (34.6%) according to the AUA model, 128 (94.1%) patients based on the Caprini score, and 80 (58.8%) patients according to the CHA2DS2-VASc score. Limitations of the study include a small sample size and lack of post-surgery venous thromboembolic events recording. CONCLUSIONS: The VTE RAMs developed by the EAU and AUA provide consistent recommendations for thromboembolism prophylaxis in urologic patients, while the Caprini model's strict adherence may lead to excessive prophylaxis recommendations. The EAU approach is user-friendly but urologists must judiciously weigh bleeding and VTE risks on an individual basis, ensuring optimal prophylaxis use.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".