What Experts Think About Prostate Cancer Management During the COVID-19 Pandemic: Report from the Advanced Prostate Cancer Consensus Conference 2021
Bibliographic record
Abstract
Patients with advanced prostate cancer (APC) may be at greater risk for severe illness, hospitalisation, or death from coronavirus disease 2019 (COVID-19) due to male gender, older age, potential immunosuppressive treatments, or comorbidities. Thus, the optimal management of APC patients during the COVID-19 pandemic is complex. In October 2021, during the Advanced Prostate Cancer Consensus Conference (APCCC) 2021, the 73 voting members of the panel members discussed and voted on 13 questions on this topic that could help clinicians make treatment choices during the pandemic. There was a consensus for full COVID-19 vaccination and booster injection in APC patients. Furthermore, the voting results indicate that the expert's treatment recommendations are influenced by the vaccination status: the COVID-19 pandemic altered management of APC patients for 70% of the panellists before the vaccination was available but only for 25% of panellists for fully vaccinated patients. Most experts (71%) were less likely to use docetaxel and abiraterone in unvaccinated patients with metastatic hormone-sensitive prostate cancer. For fully vaccinated patients with high-risk localised prostate cancer, there was a consensus (77%) to follow the usual treatment schedule, whereas in unvaccinated patients, 55% of the panel members voted for deferring radiation therapy. Finally, there was a strong consensus for the use of telemedicine for monitoring APC patients. PATIENT SUMMARY: In the Advanced Prostate Cancer Consensus Conference 2021, the panellists reached a consensus regarding the recommendation of the COVID-19 vaccine in prostate cancer patients and use of telemedicine for monitoring these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 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".