Bibliographic record
Abstract
Deep-vein thrombosis (DVT) is a common condition that can lead to complications such as postphlebitic syndrome, pulmonary embolism and death. The approach to the diagnosis of DVT has evolved over the years. Currently an algorithm strategy combining pretest probability, D-dimer testing and compression ultrasound imaging allows for safe and convenient investigation of suspected lower-extremity thrombosis. Patients with low pretest probability and a negative D-dimer test result can have proximal DVT excluded without the need for diagnostic imaging. The mainstay of treatment of DVT is anticoagulation therapy, whereas interventions such as thrombolysis and placement of inferior vena cava filters are reserved for special situations. The use of low-molecular-weight heparin allows for outpatient management of most patients with DVT. The duration of anticoagulation therapy depends on whether the primary event was idiopathic or secondary to a transient risk factor. More research is required to optimally define the factors that predict an increased risk of recurrent DVT to determine which patients can benefit from extended anticoagulant therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".