Prediction of Esophageal Varices by Liver Stiffness and Platelets in Persons With Human Immunodeficiency Virus Infection and Compensated Advanced Chronic Liver Disease
Bibliographic record
Abstract
BACKGROUND: People living with human immunodeficiency virus (PLWH) are at increased risk of cirrhosis and esophageal varices. Baveno VI criteria, based on liver stiffness measurement (LSM) and platelet count, have been proposed to avoid unnecessary esophagogastroduodenoscopy (EGD) screening for esophageal varices needing treatment (EVNT). This approach has not been validated in PLWH. METHODS: PLWH from 8 prospective cohorts were included if they fulfilled the following criteria: (1) compensated advanced chronic liver disease (LSM >10 kPa); (2) availability of EGD within 6 months of reliable LSM. Baveno VI (LSM <20 kPa and platelets >150 000/μL), expanded Baveno VI (LSM <25 kPa and platelets >110 000/μL), and Estudio de las Hepatitis Víricas (HEPAVIR) criteria (LSM <21 kPa) were applied to identify patients not requiring EGD screening. Criteria optimization was based on the percentage of EGDs spared, while keeping the risk of missing EVNT <5%. RESULTS: Five hundred seven PLWH were divided into a training (n = 318) and a validation set (n = 189). EVNT were found in 7.5%. In the training set, Baveno VI, expanded Baveno VI, and HEPAVIR criteria spared 10.1%, 25.5%, and 28% of EGDs, while missing 0%, 1.2%, and 2.2% of EVNT, respectively. The best thresholds to rule out EVNT were platelets >110 000/μL and LSM <30 kPa (HIV cirrhosis criteria), with 34.6% of EGDs spared and 0% EVNT missed. In the validation set, HEPAVIR and HIV cirrhosis criteria spared 54% and 48.7% of EGDs, while missing 4.9% and 2.2% EVNT, respectively. CONCLUSIONS: Baveno VI criteria can be extended to HEPAVIR and HIV cirrhosis criteria while sparing a significant number of EGDs, thus improving resource utilization for PLWH with compensated advanced chronic liver disease.
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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 | 0.003 |
| 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) | 0.001 | 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".