Diagnosis of deep-vein thrombosis in the year 2000
Bibliographic record
Abstract
Deep-vein thrombosis is a relatively common disease, amenable to therapy but with a potentially fatal outcome if untreated. The diagnosis can be made in most patients with the noninvasive imaging procedure ultrasonography, but limitations exist. As with all tests, there is a potential for false-positive and false-negative results. The latter are especially an issue for calf vein thrombi, and this in part has led to the concept of serial testing of the proximal venous system and not imaging the calf. The premise of the repeat (serial) test is that only thrombi that extend to the proximal system are clinically relevant and such thrombi will be detected on the repeat test. However, despite the safety of the serial testing concept, it is inconvenient and expensive. In the last few years, the diagnostic process has been improved by the validation of a clinical model that accurately categorizes patients as having low, moderate, or high probability. Among the improvements this provides is the elimination of serial testing if the ultrasonogram is normal and the clinical probability low. The fibrin degradation product D-dimer has been demonstrated to have a high negative predictive value and has also proven useful in diagnostic algorithms. The combination of the D-dimer with clinical model assessment will enable diagnostic testing strategies that are more safe, effective, and convenient for patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".