Incidentally detected splanchnic vein thrombosis: A sub-study from an international registry
Bibliographic record
Abstract
Background Splanchnic vein thrombosis (SVT) is often diagnosed incidentally. Whether demographic characteristics, underlying risk factors and therapeutic management of patients with incidentally detected SVT (IDSVT) differ from symptomatic patients is unknown. Materials and Methods Consecutive patients with objectively diagnosed SVT were eligible for a multicenter, international registry, between May 2008 and January 2012. Information on clinical presentations, risk factors and therapeutic strategies was collected and analyzed separately for asymptomatic patients. Results Out of 613 episodes of SVT 180 were incidentally detected. IDSVT patients were significantly older than symptomatic patients (56.2 and 52.1 years, respectively, p=0.0007). Gender distribution (66.7% and 60.7% males, p=0.196) and ethnicity (73.9% and 74.4% Caucasians, p=1) were similar between the two groups. Computed tomography was more commonly used as diagnostic imaging in symptomatic patients (63.7% and 73.8%, p=0.016), while ultrasonography was more frequent in IDSVT (29.1% and 19.8%, p=0.018). IDSVT patients were more likely to have single vein involvement (60.6% and 47.5%, p=0.004). Portal vein was the most frequently involved vessel in both groups (77.8% and 76.5%, p=0.806), while mesenteric (32.2% and 47.8%, p=0.0006) and splenic vein thrombosis (17.2% and 25.2%, p=0.043) was more common in symptomatic patients. Patients with IDSVT were more likely to have underlying solid cancer (34.4% and 17.6%, p
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".