Real-World Impact of Granulocyte-Colony Stimulating Factor on Febrile Neutropenia
Bibliographic record
Abstract
BACKGROUND: Primary prophylaxis with granulocyte colony-stimulating factors (pp-g-csf) is recommended in patients undergoing chemotherapy carrying a febrile neutropenia (fn) risk of 20% or more. In the present study, we examined clinical practice patterns and the impact of pp-g-csf on fn incidence in women with early-stage breast cancer (ebc) treated with modern adjuvant chemotherapy (act). METHODS: This single-centre retrospective cohort study of women with ebc, who were identified from the pharmacy database and who received at least 1 cycle of modern act from January 2009 to December 2011, was conducted at the Cancer Centre of Southeastern Ontario. Data on patient demographics, pathology, stage distribution, chemotherapy, pp-g-csf use, dose reductions, chemotherapy delays, treatment discontinuation, relative dose intensity, and fn events were collected. Chi-square tests, t-tests, univariate and multivariate logistic regression analyses, and nonparametric Mann-Whitney U-tests were used for data analysis. RESULTS: Of the 239 women eligible for analysis, 145 (61%) received pp-g-csf, and 50 (21%) developed at least 1 episode of fn. Use of pp-g-csf was associated with a significantly lower rate of fn (14% vs. 31%, p = 0.002) and trends to fewer dose delays (17% vs. 27%, p = 0.060) and dose reductions (19% vs. 25%, p = 0.28). Among women receiving pp-g-csf, higher fn rates were associated with an age of 65 years or older, taxane-based chemotherapy, and prophylaxis with filgrastim. CONCLUSIONS: Clinical practice patterns at our institution showed that more than 50% of ebc patients treated with modern act received pp-g-csf, which led to fewer fn episodes and increased delivery of planned act. The observed high fn risk despite pp-g-csf was linked to older age, taxane-based chemotherapy, and filgrastim.
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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 | 0.001 | 0.009 |
| 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 | 0.001 | 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".