Bibliographic record
Abstract
Introduction:The impact of the COVID-19 pandemic on the management of metastatic hormone-sensitive prostate cancer (mHSPC) is unknown.Recent Canadian recommendations favor the use of androgen-receptor-axis targeted therapies (ARATs) over docetaxel to minimize risk of COVID-19 infections.We aimed to characterize how the pandemic has influenced current practice patterns of medical oncologists in Canada who treat mHSPC.Methods: Using SurveyMonkey ® , we conducted an online survey among 119 practicing members of the Genitourinary Medical Oncologists of Canada (GUMOC) in January 2021.The survey consisted of 16 questions, including demographics, treatment approach before and during the pandemic, and outcomes of any COVID-19-positive patients with mHSPC.Results: Response rate was 42% (n=50).Most respondents were male (65%) and practiced in academic centers (71%).The majority (64%) described a change in practice patterns during the pandemic.For low-(LV) and high-volume (HV) mHSPC, the proportion of respondents offering ARATs increased from 73% to 79% and from 63% to 84%, respectively.Increased use of granulocyte colony-stimulating factor primary prophylaxis with docetaxel was reported by 35% of respondents, with 22% offering it to more than half of the patients on chemotherapy.Roughly half (45%) intend to maintain these practice changes beyond the pandemic.Only nine (18%) reported having mHSPC patients diagnosed with COVID-19, all of which were diagnosed prior to initiation of systemic therapy.Among these patients, 77% were treated with ARAT, none with docetaxel; four required hospitalizations (one required ICU), and all recovered.Conclusions: During the pandemic, Canadian medical oncologists reported using more ARATs and less docetaxel for patients with mHSPC.Many plan to maintain these changes post-pandemic.Further research is warranted to examine whether these practice changes impact outcomes.Encouragingly, no deaths related to COVID-19 among patients with mHSPC have been reported.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.740 | 0.371 |
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".