Nomogram prediction for prostate cancer and aggressive prostate cancer at time of biopsy: utilizing all risk factors and tumor markers for prostate cancer.
Bibliographic record
Abstract
BACKGROUND: There is a large amount of confusion in interpreting prostate specific antigen (PSA) values for prostate cancer. More precise risk assessments for prostate cancer detection are needed for men faced with an abnormal PSA. METHODS: We studied a sample of 2,637 men who underwent a prostate biopsy for an abnormal digital rectal exam (DRE) or PSA. Using factors including age, ethnicity, family history of prostate cancer, previous negative biopsy, presence of voiding symptoms, prostate volume, DRE and PSA, we constructed nomograms to predict the probability of prostate cancer at biopsy. RESULTS: Of the 2,637 men, 1,282 men (48.6%) had prostate cancer detected. Age, ethnicity, family history of prostate cancer, a previous negative biopsy, prostate volume, DRE and PSA were all significant predictors of prostate cancer. Nomograms were constructed based on these factors to predict the risk of prostate cancer and of aggressive prostate cancer (defined as a Gleason Score 7 or more). The positive predictive value varied from 5% to 95% based on the nomograms. The nomograms were validated using bootstrapping methods and the expected and observed proportions were found to be highly concordant. CONCLUSIONS: For men with an abnormal PSA or DRE, the risk for prostate cancer can be accurately estimated using a nomogram based on age, ethnicity, family history of prostate cancer, previous negative biopsy, presence of voiding symptoms, prostate volume, DRE and PSA. This tool will aid physicians and patients in determining the need for prostate biopsy.
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 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".