Everolimus and sunitinib for advanced pancreatic neuroendocrine tumors: a matching-adjusted indirect comparison
Bibliographic record
Abstract
BACKGROUND: Everolimus and sunitinib have been approved for the treatment advanced pancreatic neuroendocrine tumors, but have not been compared to each other in a randomized trial and have not demonstrated prolonged overall survival compared to placebo. This study aimed to indirectly compare overall and progression-free among everolimus, sunitinib and placebo across separate randomized trials. METHODS: A matching adjusted indirect comparison was conducted in which individual patient data from the pivotal trial of everolimus (n = 410) were adjusted to match the inclusion criteria and average baseline characteristics reported for the pivotal trial of sunitinib (n = 171). Prior to matching, trial populations differed in baseline performance status and prior treatments. After matching, these and all other available baseline characteristics were balanced between trials. RESULTS: Compared to the placebo arm in the sunitinib trial, everolimus was associated with significantly prolonged overall survival (HR = 0.61, 95% CI = 0.38-0.98, p = 0.042).Compared to sunitinib, everolimus was associated with similar progression-free (hazard ratio for death (HR) = 0.84, 95% CI = 0.46-1.53, p = 0.578) and overall survival (HR = 0.81, 95% CI = 0.49-1.31, p = 0.383). CONCLUSION: After adjusting for observed cross-trial differences, everolimus treatment was associated with longer overall survival than the placebo arm in the sunitinib trial for advanced pancreatic neuroendocrine tumors.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".