Primary neuroendocrine tumor of the pineal gland: a case report
Bibliographic record
Abstract
INTRODUCTION: Primary intracranial neuroendocrine tumors are exceedingly rare, with few cases in the literature. We present a case of a primary neuroendocrine carcinoma of the pineal gland, which is the second that has ever been reported. CASE PRESENTATION: A 53-year-old male patient presented with vomiting, weakness, and headaches. Imaging revealed a lesion in the pineal region, which was surgically resected. This mass was characterized by histology as a neuroendocrine carcinoma, given the presence of neuroendocrine markers and cytokeratin markers with absence of a primary lesion elsewhere on imaging. CONCLUSIONS: There are currently no guidelines on the management of primary intracranial neuroendocrine tumors. In this case, the patient underwent surgical resection and craniospinal radiotherapy. He subsequently received one cycle of chemotherapy with temozolomide, an alkylating agent, but he unfortunately did not tolerate treatment. A multidisciplinary decision was made along with the patient and his family to focus on palliative care. Eighteen months after the initial presentation, disease recurred in the patient's neck. The patient underwent resection to control the metastases, with a plan to follow with radiotherapy and chemotherapy. Unfortunately, the patient became unwell and died at 21 months after initial diagnosis. This demonstrates a need for continued research and reporting on this uncommon disease entity.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".