The who, when, and why of primary adrenal malignancies: Insights into the epidemiology of a rare clinical entity
Bibliographic record
Abstract
BACKGROUND: Primary malignancies of the adrenal glands are rare. Epidemiologic assessment of primary adrenal malignancies is lacking and has been limited to case reports and series. Population-level data can provide a better understanding of the incidence, distribution, and prognostic factors associated with these rare malignancies. METHODS: The Surveillance, Epidemiology, and End Results database (1973-2013) was queried for all patients who were diagnosed with primary adrenal malignancies, categorized in 5 histologic groups: adrenocortical carcinoma (ACC), pheochromocytoma and paraganglioma (PH), neuroblastoma (NE), non-Hodgkin lymphoma (NHL), and sarcoma (SA). Age-adjusted incidence, distribution trends, and cancer-specific survival (CSS) for each group were analyzed. RESULTS: In total, 4695 patients with primary adrenal malignancies were identified, including 2057 with ACC, 512 with PH, 1863 with NE, 202 with NHL, and 61 with SA. The age-adjusted incidence of all 5 histologic subtypes was rising. Age at presentation differed substantially by histologic group: NE was the most prevalent during the first decade of life, whereas ACC predominated after age 30 years, and NHL outnumbered PH after age 70 years. Patient-specific factors were not associated with advanced disease at the time of presentation. The 5-year CSS rate for each histologic subtype was 38% for ACC, 69% for PH, 64% for NE, 38% for NHL, and 42% for SA. Survival outcomes for patients with ACC, NHL, PH and SA remained unchanged over the 40-year study period. Multimodal therapy was associated with higher CSS in patients with NE. CONCLUSIONS: This first population-level analysis of all primary adrenal malignancies provides important initial data regarding presentation and clinical outcomes. Notably, except for patients with NE, the survival of patients with these rare cancers has not improved over the past 40 years.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".