Nomogram use for the prediction of indolent prostate cancer
Bibliographic record
Abstract
BACKGROUND: Screening for prostate cancer has resulted in an increased incidence-to-mortality ratio. Not all cancers deserve immediate treatment. It has therefore become more important to be able to identify those cases of screen-detected prostate cancer most likely to show indolent behavior. METHODS: The Kattan-nomogram for the prediction of indolent prostate cancer was validated and recalibrated for use in a screening setting. The recalibrated nomogram was used to calculate the number of men who were predicted to have indolent cancer in a screen-detected cohort from the European Randomized study of Screening for Prostate Cancer (ERSPC), section Rotterdam. RESULTS: Of 1629 cancers detected in 2 subsequent screening rounds 825 were suitable for nomogram use. The remainder were very unlikely to have indolent cancer. A total of 485 men (485 of 825 = 59%) were predicted to have indolent cancer, which is 30% (485 of 1629) of all screen-detected cases. Cancers found at repeated screening after 4 years had a higher probability of indolent cancer than cases from the prevalence screening (44% vs 23%; P < .001). CONCLUSIONS: The current nomogram can identify substantial groups of screen-detected cancers that are likely indolent and can therefore be considered 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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".