The effect of using long-acting octreotide as adjuvant therapy for patients with grade 2 pancreatic neuroendocrine tumors after radical resection
Bibliographic record
Abstract
Abstract Objective: To investigate the effect of long-acting octreotide as adjuvant therapy in the prevention of tumor recurrence in patients with grade 2 pancreatic neuroendocrine tumors (pNETs) after radical resection. Methods: The postoperative follow-up data of 130 patients with resectable G2 pNET treated in the Changhai Hospital from 2008 to 2018 were retrospectively analyzed: 59 patients received long-acting octreotide as adjuvant therapy for 6 to 12 months (Oct group) and 71 patients received active follow-up (control group), both of which began after the radical resection, with the primary observation endpoint of disease-free survival (DFS) and the secondary study endpoint of overall survival. Results: The median age of the patients in the Oct group and control group was 52 and 54 years, respectively. There were 28 male cases (47.5%) and 33 male cases (46.5%) in the 2 groups. The median maximum tumor diameter was 3.5 and 3.0 cm, respectively; lymph node metastasis was positive in 13 cases (22.0%) and 9 cases (12.7%); there was peripancreatic nerve invasion in 11 cases (18.6%) and 6 cases (8.5%). Survival analysis revealed that there were significant differences in 2-year DFS% (98.3% vs 88.7%, P = .0371) and 3-year DFS% (96.6% vs 85.9%, P = .0498) between the Oct group and control group. Long-acting octreotide treatment was found to reduce the risk of 3-year recurrence of G2 pNET after radical resection (HR = 0.2, P = .044) with the application of inverse-probability-of-treatment weighted to balance the limited data bias. Conclusion: Using long-acting octreotide as adjuvant therapy for G2 pNET patients after radical surgery may improve the rate of 3y-DFS, but the benefit needs to be confirmed in a well-designed random control clinical trial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".