A nomogram for predicting low‐volume/low‐grade prostate cancer
Bibliographic record
Abstract
BACKGROUND: The authors reported previously that assessment of the number of positive biopsy cores, maximum tumor length in a core, Gleason score, and prostate volume in an extended biopsy enhanced the accuracy of predicting low-volume/low-grade prostate cancer. On the basis of those findings, they developed a nomogram to predict the probability of low-volume/low-grade prostate cancer specifically for men with a single positive biopsy core. METHODS: The study cohort comprised 258 men who underwent radical prostatectomy without neoadjuvant therapy. Prostate cancer was diagnosed in only 1 core of an extended biopsy scheme. Low-volume/low-grade cancer was defined as pathologic organ-confined disease and a tumor volume<0.5 cc with no Gleason grade 4 or 5 cancer. Patient age, prostate-specific antigen (PSA) level, prostate volume, PSA density (PSAD), and tumor length in a biopsy core were examined as variables. A fitted multiple logistic regression model was used to establish the nomogram. RESULTS: One hundred thirty-three patients (51.6%) had low-volume/low-grade cancer. To establish the nomogram, age, PSAD, and tumor length were adopted as variables. The fitted model suggested that older age, higher PSAD values, and greater tumor length would reduce the probability of low-volume/low-grade cancer. The nomogram predicted low-volume/low-grade cancer with good discrimination (an area under the receiver operating characteristic curve of 0.727). Calibration of this nomogram showed good predicted probability. CONCLUSIONS: The authors established a nomogram with which to predict low-volume/low-grade cancer in men with 1 positive biopsy core in an extended biopsy scheme, and they recommend this nomogram for use in selecting men for active surveillance.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".