Nomograms Provide Improved Accuracy for Predicting Survival after Radical Cystectomy
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
AIMS: To develop multivariate nomograms that determine the probabilities of all-cause and bladder cancer-specific survival after radical cystectomy and to compare their predictive accuracy to that of American Joint Committee on Cancer (AJCC) staging. METHODS: We used Cox proportional hazards regression analyses to model variables of 731 consecutive patients treated with radical cystectomy and bilateral pelvic lymphadenectomy for bladder transitional cell carcinoma. Variables included age of patient, gender, pathologic stage (pT), pathologic grade, carcinoma in situ, lymphovascular invasion (LVI), lymph node status (pN), neoadjuvant chemotherapy (NACH), adjuvant chemotherapy (ACH), and adjuvant external beam radiotherapy (AXRT). Two hundred bootstrap resamples were used to reduce overfit bias and for internal validation. RESULTS: During a mean follow-up of 36.4 months, 290 of 731 (39.7%) patients died; 196 of 290 patients (67.6%) died of bladder cancer. Actuarial all-cause survival estimates were 56.3% [95% confidence interval (95% CI), 51.8-60.6%] and 42.9% (95% CI, 37.3-48.4%) at 5 and 8 years after cystectomy, respectively. Actuarial cancer-specific survival estimates were 67.3% (62.9-71.3%) and 58.7% (52.7-64.2%) at 5 and 8 years, respectively. The accuracy of a nomogram for prediction of all-cause survival (0.732) that included patient age, pT, pN, LVI, NACH, ACH, and AXRT was significantly superior (P=0.001) to that of AJCC staging-based risk grouping (0.615). Similarly, the accuracy of a nomogram for prediction of cancer-specific survival that included pT, pN, LVI, NACH, and AXRT (0.791) was significantly superior (P=0.001) to that of AJCC staging-based risk grouping (0.663). CONCLUSIONS: Multivariate nomograms provide a more accurate and relevant individualized prediction of survival after cystectomy compared with conventional prediction models, thereby allowing for improved patient counseling and treatment selection.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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