Information Content of Five Nomograms for Outcomes in Prostate Cancer
Bibliographic record
Abstract
In this study, we used 327 cases of localized prostate cancer to determine the information content provided by 5 popular nomograms for predicting outcomes in localized prostate cancer. All study patients underwent radical prostatectomy. For each case and each nomogram, we calculated the estimated probability of outcome, and, from this probability, we calculated the information content as 1-S, where S is the entropy. With this definition, information content is minimized at 0 and maximized at 1. We found that the average information content ranged from 0.16 for the Partin tables to 0.44 for the recent Kattan nomogram for 10-year disease-free survival. Furthermore, the Kattan 10-year nomogram provided information content greater than 0.5 for 50% of study cases, so that among these 5 nomograms, we judged its performance the best. Nevertheless, because even this nomogram provided less than 0.5 information content for 50% of our cases, we believe that it can be improved and that additional measurements or markers observed on the biopsy tissues are likely to produce better nomograms.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".