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 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".