Regional differences in stage distribution and rates of treatment for adrenocortical carcinoma across United States SEER registries
Bibliographic record
Abstract
BACKGROUND: We tested for regional differences across United States (US) in rates of adrenalectomy, systemic therapy, and adrenalectomy and systemic therapy combination for adrenocortical carcinoma (ACC) patients. We hypothesized that no differences exist, especially after accounting for baseline patient and tumor characteristics. METHODS: Within Surveillance, Epidemiology, and End Results (SEER) database (2004-2018), 1275 ACC patients were identified. Distribution of patient age, tumor size, ENSAT (European Network for the Study of Adrenal Tumors) stages, and treatments were tabulated and graphically displayed, according to nine geographical registries, corresponding to the population of specific states, cities or macro areas of the US on which the data are based on. Multinomial models predicted treatment probability for each patient according to registries. RESULTS: Patients count according to registries ranged from 62 to 509. Differences across registries existed for age (range 54-59 years; P=0.4), tumor size (8.5-11.0 cm; P=0.2), ENSAT stage (1-11% vs. 17-35% vs. 18-32% vs. 24-44%, in respectively ENSAT stage I, II, III, and IV), and treatment distribution (35-53% vs. 5-21% vs. 23-42%, in respectively adrenalectomy, systemic therapy, and adrenalectomy and systemic therapy combination; P=0.039). After adjustment for age, stage and year of diagnosis, clinically meaningful residual differences across registries remained for adrenalectomy (33-54%), systemic therapy (4-19%), and adrenalectomy and systemic therapy combination (20-38%). However, most variability originated from registries with smallest sample sizes. CONCLUSIONS: We identified important variability in ACC treatment according to SEER geographical registries, even after considering baseline patient and tumor characteristics. These findings may be indicative of differences in quality of care or expertise in ACC management.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".