Acute Abdominal Venous Thromboses—The Hyperdense CT Sign
Bibliographic record
Abstract
OBJECTIVE: To assess the ability of unenhanced computed tomography (CT) for diagnosis of acute abdominal venous thrombosis (VT). METHOD: This retrospective study matched 29 patients with 52 VT with a control group of 29 patients without VT. Two radiologists independently evaluated the unenhanced CT on standard abdominal and narrowed window settings to detect acute VT by anatomical location and confidence level. The effect of age, sex, hemoglobin, and hematocrit on VT density (HU) was also evaluated. RESULTS: Overall sensitivity, specificity, and accuracy were 77.9% (95% CI, 66%-85%), 96.7% (95% CI, 94%-98%), and 95.4% (95% CI, 93%-96%), respectively, in the detection of VT, with improved sensitivity and confidence ratings on narrowed windows. Mean VT density (Hounsfield unit) was positively associated with hemoglobin and hematocrit and significantly higher than background blood (67.2 vs 37.0; P < 0.0001). CONCLUSION: Acute abdominal VT can be detected on unenhanced CT in more than two thirds of the cases by identifying hyperdense venous segments.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".