The Impact of the Ongoing COVID-19 Epidemic on the Increasing Risk of Adverse Pathology in Prostate Cancer Patients Undergoing Radical Prostatectomy
Bibliographic record
Abstract
We aimed to assess whether the ongoing course of the COVID-19 epidemic has been associated with an increased risk of adverse pathology (AP) findings in prostate cancer (PC) patients treated with radical prostatectomy (RP). We performed a retrospective data analysis which included 408 consecutive, non-metastatic, previously untreated PC patients who underwent RP in our institution between March 2020 and September 2021. Patients were divided into two equally numbered groups in regard to the median surgery date (Early Epidemic [EE] and Late Epidemic [LE]) and compared. Adverse pathology was defined as either grade group (GG) ≥ 4, pT ≥ 3a or pN+ at RP. Patients in the LE group demonstrated significantly higher rates of AP than in the EE group (61 vs. 43% overall and 50 vs. 27% in preoperative non-high-risk subgroup, both p < 0.001), mainly due to higher rates of upgrading. On multivariable analysis, consecutive epidemic week (odds ratio: 1.02, 95% confidence interval: 1.00−1.03, p = 0.009) as well as biopsy GG ≥ 2 and a larger prostate volume (mL) were associated with AP in non-high-risk patients. The study serves as a warning call for increased awareness of risk underassessment in contemporarily treated PC patients.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".