Toxicity of targeted anticancer treatments on the liver in myeloproliferative neoplasms
Bibliographic record
Abstract
Surrogate endpoints are needed to estimate clinical outcomes in primary sclerosing cholangitis (PSC). Serum alkaline phosphatase was among the first markers studied, but there is substantial variability in alkaline phosphatase levels during the natural history of PSC without intervention. The Mayo risk score incorporates noninvasive variables and has served as a surrogate endpoint for survival for more than two decades. Newer models have better test performance than the Mayo risk score, including the primary sclerosing risk estimate tool (PREsTo) model and UK-PSC score that estimate hepatic decompensation and transplant free survival, respectively. The c-statistics for transplant-free survival for the Mayo risk model and the long-term UK-PSC model are 0.68 and 0.85, respectively. The c-statistics for hepatic decompensation for the Mayo risk model and PREsTo model are 0.85 and 0.90, respectively. The Amsterdam-Oxford model included patients with large duct and small duct PSC and patients with PSC-autoimmune hepatitis overlap and had a c-statistic of 0.68 for transplant-free survival. Other noninvasive tests that warrant further validation include magnetic resonance imaging, elastography and the enhanced liver fibrosis score. Prognostic models, noninvasive tests or a combination of these surrogate endpoints may not only serve to be useful in clinical trials of investigational agents, but also serve to inform our patients about their prognosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".