Management of patients who opt for radical prostatectomy during the coronavirus disease 2019 (COVID‐19) pandemic: an international accelerated consensus statement
Bibliographic record
Abstract
OBJECTIVE: Coronavirus disease-19 (COVID-19) pandemic caused delays in definitive treatment of patients with prostate cancer. Beyond the immediate delay a backlog for future patients is expected. The objective of this work is to develop guidance on criteria for prioritisation of surgery and reconfiguring management pathways for patients with non-metastatic prostate cancer who opt for surgical treatment. A second aim was to identify the infection prevention and control (IPC) measures to achieve a low likelihood of coronavirus disease 2019 (COVID-19) hazard if radical prostatectomy (RP) was to be carried out during the outbreak and whilst the disease is endemic. METHODS: We conducted an accelerated consensus process and systematic review of the evidence on COVID-19 and reviewed international guidance on prostate cancer. These were presented to an international prostate cancer expert panel (n = 34) through an online meeting. The consensus process underwent three rounds of survey in total. Additions to the second- and third-round surveys were formulated based on the answers and comments from the previous rounds. The Consensus opinion was defined as ≥80% agreement and this was used to reconfigure the prostate cancer pathways. RESULTS: Evidence on the delayed management of patients with prostate cancer is scarce. There was 100% agreement that prostate cancer pathways should be reconfigured and measures developed to prevent nosocomial COVID-19 for patients treated surgically. Consensus was reached on prioritisation criteria of patients for surgery and management pathways for those who have delayed treatment. IPC measures to achieve a low likelihood of nosocomial COVID-19 were coined as 'COVID-19 cold' sites. CONCLUSION: Reconfiguring management pathways for patients with prostate cancer is recommended if significant delay (>3-6 months) in surgical management is unavoidable. The mapped pathways provide guidance for such patients. The IPC processes proposed provide a framework for providing RP within an environment with low COVID-19 risk during the outbreak or when the disease remains endemic. The broader concepts could be adapted to other indications beyond prostate cancer surgery.
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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 | 0.161 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".