Radiofrequency Ablation as a Primary Therapy for Benign Functioning Insulinoma
Bibliographic record
Abstract
OBJECTIVE: Insulinomas are rare, life-threatening pancreatic neuroendocrine tumors. Surgical removal continues to be the treatment of choice, yet it is associated with considerable risk of morbidity. Here, we describe our patient with insulinoma who was successfully treated with radiofrequency ablation. METHODS: The patient was a 56-year-old man with no history of diabetes mellitus. He presented with recurrent episodes of transient ischemic attacks and stroke over the last 3 years. Some changes in his behavior and memory were noticed by family members. During his hospital stay for the second transient ischemic attack, frequent hypoglycemia was documented, which was asymptomatic. Insulinoma was confirmed biochemically. Radiological findings were also compatible with pancreatic neuroendocrine tumor. Treatment modalities were explained to the patient. However, he strongly refused surgery. Meanwhile, he was admitted because of a stroke and concurrent hypoglycemia again. In view of his refusal of the surgical treatment and due to his presentation with acute stroke and high-risk status for surgery, radiofrequency ablation was finalized. RESULTS: Radiofrequency ablation of the pancreatic tumor using 40.75 Gy over fractions was performed with a favorable outcome. The patient has achieved biochemical normalization and remained euglycemic during his follow- up. Computed tomography scan of the abdomen during follow-up showed a mild regression of the size of the tumor. CONCLUSION: This report shows a treatment challenge that required the use of an alternative treatment option other than the standard of care. It highlights the evolving evidence of radiofrequency as a therapeutic modality for patients with insulinoma.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".