Treatment patterns, toxicity, and outcomes of elderly patients with advanced pancreatic cancer receiving first-line chemotherapy.
Bibliographic record
Abstract
71 Background: Advanced pancreatic cancer (APC) has a poor prognosis despite treatment with palliative chemotherapy. Randomized trials have demonstrated improved overall survival (OS) with combination chemotherapy including 5-fluorouracil, irinotecan, leucovorin, and oxaliplatin (FOLFIRINOX) or nab-plaxictaxel and Gemcitabine (NG) compared to Gemcitabine (GEM) alone, however, combination therapy is associated with higher rates of toxicity. There is limited data regarding the efficacy and toxicity of FOLFIRINOX, NG, and GEM in elderly patients with APC. Objective: Describe the treatment patterns, toxicity, and outcomes of patients ≥ 65 years of age treated with first-line palliative FOLFIRINOX, NG, or GEM. Methods: Patients ≥ 65 diagnosed with APC from 2012-2016 and treated with palliative chemotherapy in Manitoba were identified from the Manitoba Cancer Registry. Retrospective review identified patients who received first line FOLFIRINOX, NG, or GEM. Patient and treatment characteristics including hematologic and non-hematologic toxicities, dose reductions or delays, tumour response and survival were recorded. Results: 87 patients aged ≥ 65 received palliative chemotherapy: 52 (60%) FOLFIRINOX, 21 (24%) NG, and 14 (16%) GEM, with median ages of 69 (65-84), 75 (65-88), and 73 (67-82), respectively. More patients treated with FOLFIRINOX had an ECOG 0-1 compared to other treatments (p = < 0.001). There was no difference in hematologic toxicity according to treatment group (p = 0.807). There was more non-hematologic toxicity with FOLFIRINOX (p = < 0.001), particularly neuropathy (p = 0.008), fatigue (p = < 0.001), and nausea/vomiting (p = 0.008). Tumour response was highest with FOLFIRINOX (p = 0.005), with a trend towards improved survival compared to NG and GEM (median OS 267 vs 232 and 126 days, respectively, p = 0.057). Conclusions: Many older patients with APC received FOLFIRINOX, with more toxicity, but also greater tumour response and a trend toward improved survival. This suggests that selected elderly patients can tolerate first-line FOLFIRINOX. This may be also due to effective patient evaluation and appropriate assignment to different treatments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".