Leptomeningeal Carcinomatosis Secondary to Gastroesophageal Adenocarcinoma: A Case Report and Literature Review of a Rare Occurrence
Bibliographic record
Abstract
We present a 68-year-old male with leptomeningeal carcinomatosis (LC) from gastroesophageal junction carcinoma. Three months following epirubicin, cisplatin, 5-flurouracil (ECF) chemotherapy, the patient suffered from gait imbalance, headache, and dysarthria. CT and MRI imaging revealed LC throughout the brain and spine. The patient was prescribed dexamethasone and treated with a course of palliative radiation to the whole brain, 2000cGy/5. Additionally, the regions of symptomatic disease in the spine included the top of L4 vertebrae to the bottom of the S2 vertebrae which was treated with 2000cGy/5, and the top of the C5 vertebrae to the bottom of the T4 vetebrae received 800cGy/1. The radiation treatment did provide short-term symptom control; however, the patient eventually passed away from his illness. While LC remains a devastating complication of malignant disease, it has been rarely discussed in GI tumors, specifically GE junction adenocarcinomas. Therefore treatment options must be considered using first principles based on management of LC in more common disease sites. With early detection, and for patients with good performance status, palliative radiation utilizing hypofractionated regimens to sites of symptomatic involvement may improve quality of life for this group of unfortunate people.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".