Bibliographic record
Abstract
Abstract Prostate cancer became an endemic disease in the Western world mainly because of the aging of the population. The peak age incidence hovers around 70. The introduction of prostatic‐specific antigen (PSA) assay for the diagnosis of prostate cancer resulted in a considerable shift in the detection of disease toward earlier stages that are potentially curable. In countries where PSA is widely used for the detection of prostate cancer, up to 75% of patients present with localized disease. This has given great expectations to patients and their physicians. However, conclusive proof that mortality is decreasing due to population screening is still lacking. The problem is enhanced by the heterogeneous behavior of the prostate. The International Union Against Cancer (UICC) TNM (tumor, nodules, metastasis) classification of malignant tumors is the means by which the prognosis of most solid cancers can be staged and defined at diagnosis. However, the TNM system does not include all relevant prognostic parameters in prostate cancer, especially the PSA. There is a need to evaluate prognostic factors not only at diagnosis but also after treatment. The need to fine‐tune the staging process in early prostate cancer drives the search for better prognostic markers. Needed measures to predict the outcome and support treatment decisions at diagnosis include biologic, pathologic, genetic, molecular, and other nonanatomic prognostic factors. Prognosis could be enhanced further using a neural network methodology, especially for an analysis of risk for each patient. However, despite efforts and hopes, no superior marker to the anatomic disease, extent, histologic grade, on PSA is available today. Within the scope of this chapter, we will present prognostic factors relevant for the treatment and outcomes in localized and advanced prostate cancer according to relationship to the tumor, the host, and the environment. A relevance‐based subdivision into essential, additional, and promising prognostic factors is also presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.174 | 0.104 |
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".