Bibliographic record
Abstract
In recent years, a number of modalities have been evaluated for the diagnosis of venous thromboembolism. The role of these modalities is still evolving. While ventilation-perfusion scanning is important in the diagnosis of venous thromboembolism, spiral CT scanning, MRI, and D-dimer assays are now being used more often, either exclusively or in combination with ventilation-perfusion scanning. A number of diagnostic algorithms using these modalities are currently being evaluated. Regardless of which diagnostic approach is used, the clinician must be aware of some key limitations. Spiral CT scanning has gained popularity because it is noninvasive and can rapidly identify other cardiopulmonary diseases that mimic pulmonary embolism. Its use has been limited because of its inability to detect subsegmental pulmonary emboli. D-dimer assays offer promise as rapid, inexpensive screening tools. However, the wide variability in assay performance has limited its usefulness. We recommend that if D-dimer assays are to be used in a diagnostic algorithm, the clinician be aware of the details of the assay. At present, lack of data precludes use of MRI as a primary diagnostic tool for detection of venous thromboembolism.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 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) | 0.004 | 0.003 |
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".