Screening for prostate cancer at low PSA range: The impact of digital rectal examination on tumor incidence and tumor characteristics
Bibliographic record
Abstract
OBJECTIVES: To compare tumor characteristics of screen-detected prostate cancers (PCs) either by digital rectal examination (DRE) or by prostate-specific antigen (PSA) as biopsy indication at low PSA. METHODS: Two populations with PSA between 2.0 and 3.9 ng/ml were studied. Group-1 was biopsied if DRE was suspicious (1st screening round, N = 1877). In group-2 all men were offered biopsy, regardless of DRE result (side-study in 2nd screening-round, N = 801). We compared cancer detection rates (CDRs) and tumor characteristics. RESULTS: In group-1 abnormal DRE prompted biopsy in 253 (13.5%) men (236 (93.3%) actually biopsied). Forty-nine PCs were detected, CDR 49/1877 = 2.6%. In group-2 we found 120 cancers in 666 (83.1%) men actually biopsied, CDR = 120/801 = 15.0%. Of all cancers detected, organ confinement (clinical T2) was found in 77.5% (group-1) and 96.6% (group-2; of which 99 T1c). Of all PCs 46.9% in group-1 and 15.0% in group-2 had biopsy Gleason score (GS) > or = 7. In the latter, 15.2% of T1cs were classified GS > or = 7. Considering only PCs with organ confinement or GS > or = 7 for each group, CDRs amounted to 2.0% versus 14.5% and 1.2% versus 2.3% for group-1 and group-2, respectively. CONCLUSIONS: PSA-based screening detected a considerable amount (15.2%) of potentially aggressive tumors as T1cs, but in addition large numbers of possibly insignificant cancers (T1c, GS = 6) were diagnosed. DRE seemed to detect more selectively high-grade cancers, but also missed many of these. Considering both populations and the need to detect aggressive but confined cancers, PSA as biopsy indication outperformed DRE at the price of more biopsies (13.5% vs. 100% if all would comply).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".