Bibliographic record
Abstract
Adrenocortical cancer (ACC) is a rare and often aggressive malignancy that requires multidisciplinary expertise for optimal management. It can present with symptoms of rapidly appearing excess steroid secretion or an abdominal mass, or it can be discovered incidentally. Thorough imaging and endocrine evaluations can identify the majority of ACCs amongst adrenal tumors; however, some smaller ACCs are better identified using fluorodeoxyglucose-positron emission tomography/computed tomography scan. Complete resection by an expert surgeon is the only potentially curative treatment for ACC, and tumor spillage should be avoided. Histopathology is important for diagnosis, but immunohistochemistry markers and gene profiling of the resected tumor may become superior to current staging systems to stratify prognosis. Despite complete resection in stage I-III tumors, approximately 40% of patients develop metastasis within 2 yr. Some retrospective studies indicate that adjuvant mitotane therapy prolongs disease-free survival, leading several centers to recommend its administration; prospective studies are under way to provide future evidence-based recommendations. For locally invading ACC, extensive en bloc resection is attempted, followed by adjuvant mitotane and, in selected cases, adjuvant radiotherapy. When ACC is not surgically resectable, mitotane therapy is adjusted to reach serum levels of 14-20 μg/ml. Careful replacement of glucocorticoid and mineralocorticoid deficiency after surgery or mitotane therapy is important; steroid excess from remaining tumor burden should also be controlled to avoid its morbidities. For metastatic disease, combination chemotherapy should be administered, if possible, in the context of multicenter collaborative research protocols. New insights in the molecular pathogenesis of ACC should allow the development of improved targeted therapies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".