Evaluation of prostatic specific antigen and digital rectal examination as screening tests for prostate cancer
Bibliographic record
Abstract
BACKGROUND: The 11,811 first visits and 46,751 annual follow-up visits performed since 1988 were analyzed in order to assess the efficacy of serum prostatic specific antigen (PSA) and digital rectal examination (DRE) for diagnosis of prostate cancer. METHODS: At first visit, screening included DRE and measurement of PSA using 3.0 ng/ml as upper limit of normal, demonstrated as optimal value in the course of the study. Transrectal echography of the prostate (TRUS) was performed only if PSA and/or DRE was abnormal. For elevated PSA, biopsy was performed only if PSA was above the value predicted from prostatic volume measured by TRUS. At follow-up visits, it was decided during the course of the study to use PSA alone. RESULTS: PSA was above 3.0 ng/ml in 16.6% and 15.6% of men at first and follow-up visits, respectively. Prostate cancer was found in 2.9% of men invited for screening at first visit and in only 0.4% of men at follow-up visits for a 7.1-fold decrease at follow-up visits done up to 11 years. PSA alone allowed to find 90.5% and 90. 0% of cancers at first and follow-up visits, respectively, compared to 41.1% and 25.0% by DRE alone. In the presence of normal PSA, 344 and 1,919 DREs are needed to find one prostate cancer at first and follow-up visits, respectively. A significant improvement in stage of the disease is found at follow-up (215 cancers) compared to first visits (337 cancers). Comparison made between men invited for screening and those who were not invited but screened showed no significant difference in terms of incidence and prevalence of prostate cancer as well as diagnosis of cancer as a function of age or as a function of PSA, DRE, and TRUS data. The cost for finding one case of prostate cancer is estimated at Can $2,420 and Can $7, 105 (first and follow-up visits, respectively, when PSA is used as prescreening). CONCLUSIONS: PSA used as prescreening and followed by DRE and TRUS when PSA is abnormal is highly efficient in detecting prostate cancer at a localized (potentially curable) stage since 99% of the cancers diagnosed were at such a localized stage, thus practically eliminating the diagnosis of metastatic and noncurable prostate cancer. The approach used is highly reliable, sensitive, efficient, and acceptable by the general population. The detection of clinically nonsignificant cancer is an exception.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".