Quantitative benefit–risk assessment of data from the phase III ClarIDHy study of ivosidenib versus placebo in patients with mIDH1 cholangiocarcinoma
Bibliographic record
Abstract
Background: Quantitative drug benefit-risk assessment (BRA) helps assess the magnitude of benefit and risk of new cancer therapies. This BRA aimed to summarize the evidence for the benefits and risks of ivosidenib versus placebo for the treatment of previously treated, locally advanced or metastatic mutant isocitrate dehydrogenase-1 cholangiocarcinoma using data from the pivotal phase III ClarIDHy study. Materials and methods: Cholangiocarcinoma experts determined relevant key benefit and risk criteria for ivosidenib and placebo to create a value tree and determined scales and weights. Multi-Criteria Decision Analysis modelling approaches were then applied to the ClarIDHy data to estimate the probability that the benefit-risk profile of ivosidenib was better than that of placebo. Results: = 61). The primary analysis [Scale Loss Score (SLoS) model] showed a 95.24% probability for the benefit-risk profile favoring ivosidenib versus placebo. Sensitivity analyses applying the SLoS model to alternative sets, and the linear and product models to the main and alternative sets, of benefit and risk criteria in the value tree also showed consistently high probability for the benefit-risk profile favoring ivosidenib versus placebo for all endpoints evaluated (SLoS model: >95%; linear model: >99%; product model: >94%). Similarly, the random weights analysis favored ivosidenib with all evaluated weights and results converging quickly towards the main analysis results. Conclusions: These results provide comprehensive evidence that ivosidenib is an effective treatment with a tolerable safety profile for this aggressive disease, supporting previous data (ClinicalTrials.gov NCT02989857).
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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 | 0.071 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".