Metastatic Malignant Thymoma to the Abdomen: A SEER Database Review and Assessment of Treatment Strategies
Bibliographic record
Abstract
BACKGROUND: Thymoma is a neoplasm occurring in 0.15 of 100,000 persons/year. Abdominal metastases are rare. We report the incidence of malignant thymoma (MT) and suggest imaging and treatment options for cases of abdominal metastasis. METHODS: A National Cancer Institute's Surveillance, Epidemiology and End Results database review was conducted to identify MT cases, followed by a literature review examining cases of metastases to the abdomen. Incidence rates were calculated, and symptoms, treatments, size and location of tumors, disease-free interval (DFI), and survival time were recorded. RESULTS: From 1973 to 2008, a total of 1,588 MT cases were identified (45.4 cases/year), which were extrapolated to 2,724 over 60 years. Incidence has risen from 17 cases in 1973 to 90 cases in 2008, with a larger incidence in males than females (0.23 vs. 0.17 per 100,000). There were 25 cases of abdominal metastasis (0.92%), 13 of which were asymptomatic. There was a wide variety of DFI and survival noted amongst the case reports. Multiple treatment modalities were used. CONCLUSIONS: The incidence of MT is on the rise with a male predominance. All patients should receive routine imaging to look for extrathoracic metastases as half will not have symptoms. All patients with abdominal metastases should be treated using a multimodal approach.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".