Outcome of second-line treatment (2L Tx) following <i>nab</i>-paclitaxel (<i>nab</i>-P) + gemcitabine (G) or G alone for metastatic pancreatic cancer (MPC).
Bibliographic record
Abstract
333 Background: The impact of 2L Tx in MPC is not well described. The phase III MPACT trial (N = 861) demonstrated superior efficacy for nab-P + G vs G alone for 1L Tx of MPC. This post hoc analysis examined 2L Tx use in pts in MPACT. Methods: OS was estimated by the Kaplan-Meier method, using the most updated information from MPACT. Data were summarized by type of 2L Tx. Results: 347 pts received 2L Tx. Baseline characteristics (at start of 1L) of those pts were balanced between arms and representative of the ITT population: median age, 61-62 years; 12% had > 3 metastatic sites, and ≈ 30% had Karnofsky PS 70-80 in each arm. 26% and 14% of pts in the nab-P + G and G arms, respectively, discontinued 1L Tx for adverse events. OS in the 347 pts was significantly longer for nab-P + G vs G (Table). The median time from the end of 1L Tx to death for pts receiving no 2L Tx was 2.5 and 1.6 mo in the nab-P + G and G arms, respectively (HR, 0.67; P < 0.001), less than half for those who received any 2L Tx (5.3 and 4.5 mo). 78% and 76% of pts received a 5FU/capecitabine (cape)–containing regimen as 2L Tx after nab-P + G and G and achieved 13.5 and 9.5 mo of median OS, respectively. Conclusions: 2L Tx after nab-P + G or G alone in MPC is feasible and may potentially improve pt outcomes. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".