Results of the NRG Oncology/RTOG 0848 Adjuvant Chemotherapy Question—Erlotinib+Gemcitabine for Resected Cancer of the Pancreatic Head
Bibliographic record
Abstract
PURPOSE: NRG/RTOG 0848 was designed to determine whether adjuvant radiation with fluoropyrimidine sensitization improved survival following gemcitabine-based adjuvant chemotherapy for patients with resected pancreatic head adenocarcinoma. In step 1 of this protocol, patients were randomized to adjuvant gemcitabine versus the combination of gemcitabine and erlotinib. This manuscript reports the final analysis of these step 1 data. METHODS: Eligibility-within 10 weeks of curative intent pancreaticoduodenectomy with postoperative CA19-9<180. Gemcitabine arm-6 cycles of gemcitabine. Gemcitabine+erlotinib arm-gemcitabine and erlotinib 100 mg/d. Two hundred deaths provided 90% power (1-sided α=0.15) to detect the hypothesized OS signal (hazard ratio=0.72) in favor of the arm 2. RESULTS: From November 17, 2009 to February 28, 2014, 163 patients were randomized and evaluable for arm 1 and 159 for arm 2. Median age was 63 (39 to 86) years. CA19-9 ≤90 in 93%. Arm 1: 32 patients (20%) grade 4 and 2 (1%) grade 5 adverse events; arm 2, 27 (17%) grade 4 and 3 (2%) grade 5. GI adverse events, arm 1: 22% grade ≥3 and arm 2: 28%, (P=0.22). The median follow-up (surviving patients) was 42.5 months (min-max: <1 to 75). With 203 deaths, the median and 3-year OS (95% confidence interval) are 29.9 months (21.7, 33.4) and 39% (30, 45) for arm 1 and 28.1 months (20.7, 30.9) and 39% (31, 47) for arm 2 (log-rank P=0.62). Hazard ratio (95% confidence interval) comparing OS of arm 2 to arm 1 is 1.04 (0.79, 1.38). CONCLUSIONS: The addition of adjuvant erlotinib to gemcitabine did not provide a signal for increased OS in this trial.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".