Improvement of platelets after SVR among patients with chronic HCV infection and advanced hepatic fibrosis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Patients with chronic hepatitis C virus (HCV) infection may develop cirrhosis with portal hypertension, reflected by decreased platelet count and splenomegaly. This retrospective cohort study aimed to assess changes in platelet counts after antiviral therapy among chronic HCV-infected patients with advanced fibrosis. METHODS: Platelet counts and spleen sizes were recorded in an international cohort of patients with Ishak 4-6 fibrosis who started antiviral therapy between 1990 and 2003. Last measured platelet counts and spleen sizes were compared with their pre-treatment values (within 6 months prior to the start of therapy). All registered platelet count measurements from 24-week following cessation of antiviral therapy were included in repeated measurement analyses. RESULTS: This study included 464 patients; 353 (76%) had cirrhosis and 187 (40%) attained sustained virological response (SVR). Among patients with SVR, median platelet count, increased by 35 × 10(9) /L (IQR 7-62, P < 0.001). In comparison, patients without SVR showed a median decline of 17 × 10(9) /L (IQR -5-47, P < 0.001). In a subgroup of 209 patients, median decrease in spleen size was 1.0 cm (IQR 0.3-2.0) for patients with SVR, while median spleen size increased with 0.6 cm (IQR -0.1-2.0, P < 0.001) among those without SVR. The changes in spleen size and platelet count were significantly correlated (R = -0.41, P < 0.001). CONCLUSIONS: Among chronic HCV-infected patients with advanced hepatic fibrosis, the platelet counts improved following SVR and the change in platelets correlated with the change in spleen size following antiviral therapy. These results suggest that HCV eradication leads to reduced portal pressure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".