Incidence and predictors of febrile neutropenia in patients with metastatic castrate- resistant prostate cancer receiving docetaxel
Bibliographic record
Abstract
Abstract Purpose The incidence of febrile neutropenia (FN) in adults with castrate-resistant metastatic prostate cancer (mCRPC) receiving docetaxel in real-world settings since the expanded role of hormonal treatments has not been well studied. The study objective is to determine the incidence of FN and neutropenia among adults with mCRPC receiving docetaxel. Secondary objectives are to quantify outcomes of patients who develop FN and to identify predictors for FN in this population. Methods A single-centre retrospective cohort study was conducted which included adults with mCRPC receiving docetaxel at the Ottawa Hospital over a 5-year period. Charts were reviewed to collect clinical data to determine the incidence of FN. A multiple logistic regression was used to identify predictors of FN. Results In patients receiving docetaxel for mCRPC, the incidence of FN and neutropenia was 34/137 (25%) and 45/137 (33%), respectively. Among 34 patients who developed FN, 94% required hospitalization for FN for a mean of 5 days (+/- 2.8) and 6% died. Following FN, 53% required at least 1 treatment delay, 71% had at least 1 dose reduction and 18% received secondary prophylaxis with WBC Growth-Factors. Age category [OR 2.025, 95% CI 1.13–3.627] and presence of multiple comorbidities [OR 1.466, 95% CI 1.01–2.258] increased the risk of FN. Conclusion The incidence of FN and neutropenia in the clinical setting in patients receiving docetaxel for mCRPC is higher than previously reported and high enough to consider primary prophylaxis in high-risk groups. Age and multiple comorbidities were identified as risk factors.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".