Real-World Use of Granulocyte-Colony Stimulating Factor in Patients with Breast Cancer from Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: There are limited published data in the Canadian healthcare system on the use of granulocyte colony-stimulating factor (G-CSF) among patients with breast cancer. This study characterized real-world G-CSF use during the period surrounding the introduction of filgrastim biosimilar. METHODS: Electronic medical records were reviewed retrospectively for patients with breast cancer who received moderately or highly myelosuppressive (neo)adjuvant chemotherapy from 2008 to 2019 in Alberta, Canada. Trends in G-CSF usage were plotted to elucidate temporal variations and multivariable regression models were constructed to identify clinical factors associated with G-CSF use. RESULTS: = 217). G-CSF use increased linearly more than two-fold over the 11-year study period. Predictors of G-CSF use included younger age, south zone of residence, higher neighborhood education, inferior disease stage, highly neutropenic risk chemotherapy, and more recent chemotherapy initiation. CONCLUSIONS: Despite increasing G-CSF usage over time, an appreciable proportion of patients for whom G-CSF prophylaxis is recommended did not receive it. G-CSF use could be further optimized to align with supportive care clinical guidelines and reduce the impact of neutropenia and its associated complications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".