Comparison of the Pauda and the Autar DVT Risk Assessment Scales in Prediction of Venous Thromboembolism in ICU Patients
Bibliographic record
Abstract
Background: The evaluation of VTE risk using risk assessment scales for each hospitalized patient is recommended by the National Institute for Health and Care Excellence. The purpose of this study was to compare the predictive accuracy of two common assessment scales, the Autar and Padua deep vein thrombosis (DVT) risk assessment scales. Methods: This prospective cohort study was conducted on 228 ICU hospitalized patients. The risk of VTE was estimated using the Autar and Padua scales during the first 48 hours after admission. The predictive accuracy of the above two risk assessment scales for VTE in ICU patients was compared based on the area under the receiver operating curve (ROC). Results: = 0.19). Moreover, the accuracy of the Autar scale and Padua obtained 24% and 14% respectively. Both scales had 100% sensitivity but their specificity was low (Autar 14% and Padua 3%). The positive likelihood ratios (LR+) were 1.17 for Autar and 1.03 for Padua. The negative likelihood ratios (LR-) were 0 for Autar and 0.89 for Padua. Inter-rater agreement values obtained 0.99 and 0.95 respectively for the the Autar and Padua scales. Conclusion: The AUC, accuracy, and LR+ of the Autar risk assessment scale were higher than the Padua scale in predicting VTE. However, both scales had excellent reliability, high sensitivity and low specificity. It is recommended that the risk of VTE is recorded by the Autar scale for patients admitted to ICUs. It can help the healthcare team in the use of prophylaxis for those that are at high risk for VTE.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".