External validation of a nomogram predicting mortality in patients with adrenocortical carcinoma
Bibliographic record
Abstract
OBJECTIVE: To develop nomograms predicting cancer-specific and all-cause mortality in patients managed with either surgery or no surgery for adrenocortical carcinoma (ACC). PATIENTS AND METHODS: The models were developed in 205 patients with ACC and externally validated using 207 other patients with ACC, identified in the 1973-2004 Surveillance, Epidemiology and End Results database. The predictors comprised age, gender, race, stage and surgery status. Nomograms based on Cox regression model-derived coefficients were used for predicting the cancer-specific and all-cause mortality, and were tested using area under the receiver operating characteristics (ROC) curve. RESULTS: In cancer-specific analyses, the median survival of patients within the development cohort was 26 months, vs 71 months in the external validation cohort (P < 0.001). In overall survival analyses, the median values were 21 vs 32 months for, respectively, the development and the external validation cohort (P < 0.001). Three variables (age, stage and surgical status) were included in the nomograms predicting cancer-specific and all-cause mortality. In the external validation cohort, the nomograms achieved between 72 and 80% accuracy for prediction of cancer-specific or all-cause mortality at 1-5 years after either surgery or diagnosis of ACC for non-surgical patients. CONCLUSION: Our models are the first standardized and individualized prognostic tools for patients with ACC. Their accuracy was confirmed within a large external population-based cohort of patients with ACC.
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".