Significant upgrading affects a third of men diagnosed with prostate cancer: predictive nomogram and internal validation
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
OBJECTIVE: To explore the rate of significant upgrading from biopsy to radical prostatectomy (RP) specimens in a contemporary cohort, and to develop a prognostic model capable of predicting the probability of significant upgrading, as previous reports indicate that up to 43% of men with low-grade prostate cancer at biopsy will be diagnosed with high-grade cancer at RP. PATIENTS AND METHODS: The study cohort comprised 4789 men (median age 63 years, range 39-82) treated with RP, with available clinical stage, prostate-specific antigen levels, biopsy and RP Gleason sum values. These variables were used as predictors in multivariate logistic regression models (LRMs) addressing the rate of significant Gleason sum upgrading, defined as a Gleason sum increase either from < or = 6 to > or = 7 or from 7 to > or = 8 between the biopsy and RP specimens. Regression coefficients were used to develop and validate (200 bootstrap re-samples) a nomogram predicting significant biopsy Gleason sum upgrading. RESULTS: Significant biopsy Gleason sum upgrading was recorded in 1349 (28.2%) patients. In multivariate LRMs, all predictors were highly significant (all P < 0.001). The bootstrap-corrected accuracy of the nomogram predicting the probability of significant Gleason sum upgrading between biopsy and RP specimens was 75.7%. CONCLUSION: Our nomogram might prove highly useful when the possibility of a more aggressive Gleason variant could change the 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 | 0.000 | 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 it