The Saudi clinical practice guideline for the diagnosis of the first deep venous thrombosis of the lower extremity
Bibliographic record
Abstract
The diagnosis of deep venous thrombosis (DVT) may be challenging due to the inaccuracy of clinical assessment and diversity of diagnostic tests. On one hand, missed diagnosis may result in life-threatening conditions. On the other hand, unnecessary treatment may lead to serious complications. As a result of an initiative of the Ministry of Health of the Kingdom of Saudi Arabia (KSA), an expert panel led by the Saudi Association for Venous Thrombo-Embolism (SAVTE; a subsidiary of the Saudi Thoracic Society) with the methodological support of the McMaster University Working Group, produced this clinical practice guideline to assist healthcare providers in evidence-based clinical decision-making for the diagnosis of a suspected first DVT of the lower extremity. Twenty-four questions were identified and corresponding recommendations were made following the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. These recommendations included assessing the clinical probability of DVT using Wells criteria before requesting any test and undergoing a sequential diagnostic evaluation, mainly using highly sensitive D-dimer by enzyme-linked immunosorbent assay (ELISA) and compression ultrasound. Although venography is the reference standard test for the diagnosis of DVT, its use was not recommended.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".