Improving the accuracy of long‐term prognostic estimates in hepatitis C virus infection
Bibliographic record
Abstract
Obtaining unbiased estimates of HCV prognosis is difficult because of potential biases associated with study design and calculation methods. We propose a new method for estimating fibrosis progression rates. A Markov model with fibrosis health states (F0-F4) was created. The maximum likelihood method was used to estimate stage-specific progression rates. We compared the standard method to the new method using two well-known cohort studies. The known stage distribution at the end of follow-up was compared with stage predicted by the Markov model using both methods of calculating transition rates. We also compared rates obtained using both methods to known fibrosis rates in a series of Monte Carlo simulations. For Kenny-Walsh's study (1999), transition rates between F0-F1, F1-F2, F2-F3, and F3-F4 were 0.042, 0.045, 0.097 and 0.070 fibrosis units/year (new method) and 0.045 units/year (standard method). The new method predicted fibrosis stage and known transition rates in Monte Carlo simulations more accurately. The standard method underestimates 30-year cirrhosis rates by up to 40%. The new (Markov maximum likelihood or MML) method allows accurate estimation of stage-specific transition probabilities from the many studies in which only a single biopsy is available. Application of the method supports the hypothesis that rates of fibrosis vary between stages.
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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 | 0.061 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".