Contemporary conditional cancer‐specific survival rates in surgically treated adrenocortical carcinoma patients: A stage‐specific analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We examined the effect of disease-free interval (DFI) duration on cancer-specific mortality (CSM)-free survival, otherwise known as the effect of conditional survival, in surgically treated adrenocortical carcinoma (ACC) patients. METHODS: Within the Surveillance, Epidemiology, and End Results database (2004-2018), 867 ACC patients treated with adrenalectomy were identified. Conditional survival estimates at 5-years were assessed based on DFI duration and according to stage at presentation. Separate Cox regression models were fitted at baseline and according to DFI. RESULTS: Overall, 406 (47%), 285 (33%), and 176 (20%) patients were stage I-II, III and IV, respectively. In conditional survival analysis, providing a DFI of 24 months, 5-year CSM-free survival at initial diagnosis increased from 66% to 80% in stage I-II, from 35% to 66% in stage III, and from 14% to 36% in stage IV. In multivariable Cox regression models, stage III (hazard ratio [HR]: 2.38; p < 0.001) and IV (HR: 4.67; p < 0.001) independently predicted higher CSM, relative to stage I-II. The magnitude of this effect decreased over time, providing increasing DFI duration. CONCLUSIONS: In surgically treated ACC, survival probabilities increase with longer DFI duration. This improvement is more pronounced in stage III, followed by stages IV and I-II patients, in that order. Survival estimates accounting for DFI may prove valuable in patients counseling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".