A retrospective review of primary prophylaxis with granulocyte-colony stimulating factor (G-CSF) for patients with genitourinary malignancies receiving chemotherapy during the COVID-19 pandemic and implications for the future.
Bibliographic record
Abstract
115 Background: To mitigate the risks of chemotherapy associated neutropenia, during the COVID-19 pandemic, all genitourinary (GU) cancer patients treated with chemotherapy at the Princess Margaret Cancer Centre (PMCC) were offered primary prophylaxis with GCSF. We hypothesize that this reduced rates of febrile neutropenia, hospitalizations, healthcare costs and improved overall outcomes, compared to GU cancer patients treated with chemotherapy without GCSF in the 2 years prior to the pandemic. Methods: We performed a retrospective review of GU cancer patients, receiving curative or palliative intent chemotherapy, with or without primary GCSF prophylaxis between January 2018 and June 2022. GCSF was given either as a single dose or as consecutive doses post chemotherapy. Main outcomes were incidence of febrile neutropenia, hospitalization, health care expenditures as well as disease specific outcomes. Results: Overall, 248 patients with prostate cancer (44%), urothelial cancers (33%) germ cell (21%), and rare GU cancers (4%) were identified. Median age was 70 (range 19-91), 92% were male, 65% were ECOG 0/1. Treatment intent was neoadjuvant (13%), adjuvant (20%), or palliative (67%). Main regimens used were docetaxel, cabazitaxel, carboplatin, cisplatin/etoposide, gemcitabine/cisplatin and BEP. Median follow-up was 10.5 months (0.23-52.3 months). A total of 206/248 received primary GCSF prophylaxis. During chemotherapy, the median white blood cell levels were higher in the GCSF group compared to the non-GCSF group (14.1*10*9/L vs 2.90*10*9/L, p<0.0001); and neutropenia rates were markedly lower (2% vs. 93%, P=<0.0001). Hospital admission rates were significantly lower in G-CSF users compared to non-users (19% vs. 69%, P<0.0001). Symptomatic disease progression 13% was the leading cause of admission in the G-CSF group. Infectious causes such as UTI, pneumonia, COVID-19, and sepsis were seen in only 12% of the G-CSF group compared to 31% in the non-users. G-CSF was generally well tolerated with just 0.97% discontinuing G-CSF. Conclusions: During the COVID-19 pandemic, primary prophylactic G-CSF use in GU cancer patients, undergoing chemotherapy significantly lowered rates of both febrile neutropenia and hospitalizations and could be a cost-effective strategy in this patient population that warrants further study.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".