Sequence of Therapy and Survival in Patients with Advanced Pancreatic Neuroendocrine Tumours
Bibliographic record
Abstract
Background: Pancreatic neuroendocrine tumours (pnets) often present as advanced disease. The optimal sequence of therapy is unknown. Methods: Sequential patients with advanced pnets referred to BC Cancer between 2000 and 2013 who received 1 or more treatment modalities were reviewed, and treatment patterns, progression-free survival (PFS), and overall survival (OS) were characterized. Systemic treatments included chemotherapy, small-molecule therapy, and peptide receptor radionuclide therapy. Results: In 66 cases of advanced pNETs, median patient age was 61.2 years (25%–75% interquartile range: 50.8–66.2 years), and men constituted 47% of the group. First-line therapies were surgery (36%), chemotherapy (33%), and somatostatin analogues (32%). Compared with first-line systemic therapy, surgery in the first line was associated with increased PFS and OS (20.6 months vs. 6.3 months and 100.3 months vs. 30.5 months respectively, p < 0.05). In 42 patients (64%) who received more than 1 line of therapy, no difference in os or pfs between second-line therapies was observed. Conclusions: Our results confirm the primary role of surgery for advanced pNETs. New systemic treatments will further increase options.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".