Men with High Prostate Specific Antigen Have Higher Risk of Gleason Upgrading after Prostatectomy: A Systematic Review and Meta-analysis.
Bibliographic record
Abstract
PURPOSE: To examine the correlation between prostate specific antigen (PSA) and the risk of Gleason sum upgrading (GSU) from biopsy Gleason sum (bGS) to prostatectomy Gleason sum (pGS). MATERIALS AND METHODS: Five electronic databases (Web of Science, Ovid Medline, Ovid Embase, SCOPUS and the Cochrane Library) were searched from inception until March 2020. Studies were included if they focused on the relationship between PSA and GSU analyzed in multivariable analysis. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were utilized. Quality of included studies was appraised utilizing the Newcastle-Ottawa Quality Assessment Scale (NOS) for case-control studies. The publication bias was evaluated by funnel plot and Egger's test. RESULTS: Our search yielded 19 studies with high quality including 42193 patients. GSU was found in 28.2% of patients. Higher PSA level was associated with a significant increased risk of GSU (pooled OR = 1.14, 95% CI: 1.10-1.18; P < .05; I2 = 92%). For the definition of upgrading from bGS ≤ 6 to pGS ≥ 7, the odds of upgrading with higher PSA level as opposed to lower PSA level was 1.12 (95% CI: 1.11-1.14; P < .05; I2 = 13%), while the odds of upgrading with other definitions were 1.11 (95% CI: 1.05-1.18; P < .05; I2 = 89%). CONCLUSION: Patients with high level of serum PSA are at high risk of undergoing pathologic upgrading at prostatectomy. Combined with other risk factors, PSA prompts risk reclassification and improve confidence of urologists in management decisions for optimal therapy. Nevertheless, further robust studies are necessitated to confirm these results.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".