The Characteristics and Trends in Adrenocortical Carcinoma: A United States Population Based Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Data on the incidence of ACC, however, are scarce and not recent. The purpose of this study was to characterize the tumor and the patients developing ACC over the last four decades using a large population based database. METHODS: We identified all cases of ACC diagnosed between 1973 - 2014 from the Surveillance, Epidemiology, and End Results-18 registry. Descriptive analyses were used for all extracted demographic, clinical, pathological, therapeutic and survival data, and were compared between the four time periods of 1973 to 1984, 1985 to 1994, 1995 to 2004 and 2005 to 2014 using Chi-square tests for categorical variables and one-way analysis of variance for continuous variables. RESULTS: There were a total of 2,014 cases of ACC between 1973 and 2014 with an age-adjusted incidence of 1.02 per million populations. The median age at diagnosis was 55 years with the majority of them being females and whites. The proportion of cases by different genders, races and age at diagnosis had not changed significantly over time. These malignancies were mostly the only primary malignancy, unilateral and of high grades at diagnosis. Surgical resection of the tumor remained the mainstay of treatment. However, there was a significant increase in the use of adjuvant radiotherapy, adjuvant chemotherapy and chemotherapy alone in recent times. The median survival time was 17 months, but continues to decrease in recent time periods. CONCLUSIONS: ACC continues to be a rare malignancy in the United States. However, most cases continue to be diagnosed only in advanced stages and are associated with poor survival. These findings underline the need for specific diagnostics tools with new and more effective treatment options.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it