Free PSA and prostate volume on the diagnosis of prostate carcinoma
Bibliographic record
Abstract
OBJECTIVE: To analyse the influence of prostate volume on the performance of total prostate specific antigen (tPSA) and free PSA (fPSA) on the diagnosis of prostate adenocarcinoma. METHODS: A total of 188 patients underwent transrectal ultrasound guided biopsies (10-12 cores) due to prostate nodes detected by digital rectal examination and/or tPSA range of 2.5-10ng/ml. Mean age was 65.7±8.7 years. 19/100 (19%)(GI) patients with prostate volume >40ml had prostate cancer while the corresponding figure for patients with prostate <40ml was 26/88 (29.5%)(GII). We analyzed the sensitivity and specificity of tPSA at cut-off points of 2.5 and 4ng/ml as well as the influence of the ratio f/tPSA in both groups of patients. RESULTS: In the group GI tPSA sensitivity and specificity were 94.4% and 19.5% at the cut-off level of 4ng/ml and 100% and 6% at 2.5ng/ml. The corresponding values for GII were 76.5% and 62.9%, and 100% and 19.3%. In group GI a cut-off of 19% for the ratio f/tPSA kept tPSA sensitivity over 90% while the specificity increased to 46.2% at cut-off level of 4ng/ml and to 32.9% at 2.5ng/ml. In the group GII the ratio f/tPSA was not able to increase the specificity of tPSA at a cut-off level of 4ng/ml without an expressive reduction of sensitivity. On the other side, for this group a cut-off of 16% for the f/tPSA ratio rose the specificity to 46.7% for a sensitivity over 90%. CONCLUSION: We recommend stratification of patients according to prostate volume to define tPSA cut-off point. The cut-off level of 2.5ng/ml for tPSA combined with f/tPSA ratio of 19% in prostates >40ml and 16% in prostates <40ml was a better option for prostate biopsy indication than tPSA at a cut-off of 4ng/ml associated or not with f/tPSA ratio.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".