Outcome analysis of prostate cancer patients with pre-treatment PSA greater than 50 ng/ml.
Bibliographic record
Abstract
INTRODUCTION: The optimal management of prostate cancer patients presenting with prostate specific antigen (PSA) levels greater than 50 ng/ml is controversial. The purpose of this study was to investigate factors associated with overall survival and biochemical outcome in a high-risk prostate cancer population with PSA>50.0 ng/ml at time of diagnosis, and no clinical or radiological evidence of metastatic disease. MATERIALS AND METHODS: A single institution chart review was conducted at the London Regional Cancer Program on 138 patients who presented with PSA levels greater than 50 ng/ml. Forty-eight (34.8%) of these patients had no clinical or radiological evidence of metastatic disease at time of diagnosis. Patient, tumor, and treatment related variables and biochemical/clinical outcomes were collected for analysis. Median follow-up was 49.4 months. Descriptive and univariable/multivariable analyses were performed in order to assess prognostic factors for freedom from biochemical failure and overall survival. RESULTS: On univariate analysis, clinical T-stage, Gleason score, primary RT, and PSA measurements including initial PSA, nadir PSA, change in PSA and respective log values were prognostic of biochemical failure. On multivariate analysis, log nadir PSA was prognostic of biochemical failure. No prognostic variables were significant for overall survival in this analysis. CONCLUSIONS: High-risk prostate cancer patients with PSA>50 ng/ml and no evidence of metastatic disease have survival characteristics are similar to other high-risk populations reported in the literature, and should be considered for aggressive therapy. The logarithm of the PSA nadir was found to predict for durable biochemical control on multivariable analysis.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".