Review of practice patterns of primary granulocyte-colony stimulating factor prophylaxis and impact on febrile neutropenia rate and chemotherapy delivery in patients with early breast cancer treated with modern adjuvant chemotherapy.
Bibliographic record
Abstract
e19541 Background: Febrile neutropenia (FN) is a major complication of chemotherapy and is associated with substantial morbidity and mortality. Primary prophylaxis with granulocyte colony-stimulating factors (PP-G-CSF) is recommended for regimens with FN risk above 20%. We undertook a review of practice patterns of PP-G-CSF and impact on febrile neutropenia rate and chemotherapy delivery in patients with early breast cancer (EBC) treated with modern adjuvant chemotherapy (ACT) at the Cancer Centre of Southeastern Ontario. Methods: Women with EBC receiving at least one cycle of modern ACT (taxane containing regimens with or without anthracyclines) between January 2009 and December 2011 were included in our review. We collected demographic, disease-related, treatment-related, and outcome variables for each patient. Results: 239 women were treated with modern ACT during the specified period and were included in our analysis. Median age was 55 (32-80); 197 (82%) were <65 years and 148 (62%) were postmenopausal. 212 (89%) patients were treated with an anthracycline-taxane (A-T) regimen and 27 (11%) with a taxane (T) regimen. 145 patients (61%) received PP-G-CSF: 47 (32%) with filgrastim (F) and 98 (68%) with pegfilgrastim (PF). Of all patients 50 (21%) developed at least one FN episode. PP-G-CSF led to lower FN rate (15% vs 31%; p=0.002), reduced dose delay (17% vs 27%; p=0.060) and dose reduction (19% vs 25%; p=0.28) of planned ACT. In women receiving PP-G-CSF higher rate of FN was associated with age ≥ 65 years (27% vs 12% if < 65 years; OR 2.8, p=0.053); T regimen (30% vs 12% for A-T; OR 3.1, p=0.041) and F use (28% [n=13] vs 8% [n=8] with PF; OR 4.3, p=0.003). Conclusions: In this single-centre retrospective cohort study patients with EBC treated with modern ACT had a high rate of FN (31%) without PP-G-CSF. More than half (61%) of the patients received PP-G-CSF which led to fewer FN episodes and increased delivery of planned ACT; however, the risk of FN remained high at 15%. Factors linked to higher rate of FN despite PP-G-CSF were older age, taxane-based chemotherapy and use of 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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".