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 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 | 0.000 |
| 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) | 0.000 | 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 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".