Bibliographic record
Abstract
The accuracy of noninvasive testing for the diagnosis of deep vein thrombosis (DVT) generally is less in asymptomatic patients than it is in those with symptoms suggestive of thrombosis. This is because asymptomatic DVT often is confined to the distal veins and, when it involves the proximal veins, the thrombi usually are smaller than in symptomatic patients with proximal thrombosis. Because the positive predictive value of noninvasive tests for asymptomatic DVT generally is 80% or less, abnormal results should be confirmed by venography. There are two main reasons why asymptomatic DVT is sought in the postoperative period: (1) to identify the need for full-dose anticoagulant therapy to prevent symptomatic episodes of venous thromboembolism (VTE), including fatal pulmonary embolism (this represents a form of secondary prophylaxis), and (2) to use this outcome as a surrogate for episodes of clinically important VTE in studies that are designed to evaluate methods of venous thrombosis prophylaxis. In relation to the first of these indications, evidence suggests that routine surveillance of high-risk patients to detect asymptomatic postoperative DVT does not result in improved clinical outcomes in patients who received appropriate VTE prophylaxis. In relation to the second indication, there is concern that asymptomatic VTE may not be a reliable surrogate for clinically important VTE, particularly if the effectiveness of different antithrombotic agents is being compared. Coupled with the comparatively low accuracy of noninvasive testing for asymptomatic DVT, this suggests that the results of such testing are unsuitable for the evaluation of new methods of prophylaxis in clinical trials.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".