Prognostic factors (PFs) of survival in a randomized phase III trial (MPACT) of weekly <i>nab</i>-paclitaxel (<i>nab</i>-P) plus gemcitabine (G) versus G alone in patients (pts) with metastatic pancreatic cancer (MPC).
Bibliographic record
Abstract
4059^ Background: In MPACT, pts who received nab-P + G vs G had improved overall survival (OS; median 8.5 vs 6.7 mo; HR 0.72; p= 0.000015). Here we assessed potential PFs of OS. Methods: 861 pts with MPC were randomized 1:1, stratified by region, presence of liver metastases, and Karnofsky performance status (KPS), to nab-P + G or G. OS was described in subgroups. A step-wise multivariate analysis (with significance level for entry of 0.20 and for stay of 0.10) was performed to evaluate the treatment effect and identify possible predictors of OS. Results: Pts with poorer PFs had a shorter median OS, consistent with the literature, and OS consistently favored nab-P + G in pts with these PFs (Table). Region of Eastern Europe, age ≥ 65 years, poorer KPS, presence of liver metastases, and number of metastatic sites all predicted OS (increased risk of death). The treatment effect remained significant (HR 0.72; 95% CI, 0.605 - 0.849; p < 0.0001, Cox proportional hazards [CPH] model). In another multivariate analysis in which baseline CA19-9 was added to the final model described above, the treatment effect HR was 0.67 (95% CI, 0.573 - 0.794; p < 0.0001, CPH model). Baseline CA19-9, a predictor of OS by univariate analysis, was not predictive after correction for the above factors. Conclusions: In MPACT, the most important predictors of OS were KPS, age, presence of liver metastases, number of metastatic sites, and region. After correcting for these factors, assignment to nab-P + G was an independent significant predictor of improved survival. Clinical trial information: NCT00844649. [Table: see text]
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".