Association between time to disease progression end points and overall survival in patients with neuroendocrine tumors
Bibliographic record
Abstract
Abstract: Overall survival can be difficult to determine for slowly progressing malignancies, such as neuroendocrine tumors. We investigated whether time to disease progression is positively associated with overall survival in patients with such tumors. A literature review identified 22 clinical trials in patients with neuroendocrine tumors that reported survival probabilities for both time to disease progression (progression-free survival and time to progression) and overall survival. Associations between median time to disease progression and median overall survival and between treatment effects on time to disease progression and treatment effects on overall survival were analyzed using weighted least-squares regression. Median time to disease progression was significantly associated with median overall survival (coefficient 0.595; P =0.022). In the seven randomized studies identified, the risk reduction for time to disease progression was positively associated with the risk reduction for overall survival (coefficient on −ln[HR] 0.151; 95% confidence interval −0.843, 1.145; P =0.713). The significant association between median time to disease progression and median overall survival supports the assertion that time to disease progression is an alternative end point to overall survival in patients with neuroendocrine tumors. An apparent albeit not significant trend correlates treatment effects on time to disease progression and treatment effects on overall survival. Informal surveys of physicians’ perceptions are consistent with these concepts, although additional randomized trials are needed. Keywords: neuroendocrine tumors, progression-free survival, disease progression, mortality
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".