Cirrhotic Right and Left Portal Veins
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to identify threshold right and left portal vein sonographic velocities that are correlated with subsequent development of hepatofugal flow in the main portal vein (MPV), a marker of portal hypertension. METHODS: A database containing 6019 Doppler liver ultrasound reports from an academic hospital was parsed using a Visual Basic computer algorithm. Right and left portal vein velocities were identified from 65 patients who developed hepatofugal MPV flow. Patients with a liver transplant or transjugular intrahepatic portosystemic shunt were excluded. Similarly, right and left portal vein velocities were identified from 195 patients free of chronic hepatic disease. The right and left portal vein velocities of these 2 groups were analyzed using a receiver operating characteristic curve to identify threshold velocities with the optimal sensitivity and specificity for patients who will develop hepatofugal flow in the MPV. RESULTS: A threshold velocity of 11 cm/s in the right portal vein is associated with 81.8% sensitivity and 93.5% specificity in distinguishing patients who develop hepatofugal flow from otherwise healthy control subjects. Likewise, a threshold velocity of 8 cm/s in the left portal vein is associated with a 62.3% sensitivity and a 94.5% specificity. CONCLUSIONS: A threshold right portal vein velocity of 11 cm/s can be used with high sensitivity and specificity to identify patients who may develop hepatofugal flow in the MPV. A left portal vein velocity less than 8 cm/s is 94.5% specific for the development of hepatofugal flow.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".