Population-based patterns of granulocyte colony stimulating factor (GCSF) use in breast cancer (BrCa) patients receiving myelosuppressive chemotherapy.
Bibliographic record
Abstract
9625 Background: Prophylaxis with GCSF can reduce hospitalization due to neutropenic fever, but early studies show that use of GCSF is frequently suboptimal. Our aims were to 1) characterize patterns of GCSF use in a population-based cohort of BrCa patients, 2) determine the rate of neutropenia and neutropenic fever in those who received and did not receive GCSF, and 3) identify patient and physician factors associated with appropriate GCSF prophylaxis. Methods: Patients diagnosed with BrCa from January to December 2008, seen at any 1 of 5 regional cancer centers in British Columbia, Canada and treated with chemotherapy protocols that posed >20% risk of neutropenic fever were reviewed. Using regression models that adjusted for confounders, the relationship between GCSF use and 1) various patient and physician characteristics and 2) treatment outcomes, such as neutropenic fever, were analyzed. Results: A total of 525 women were included: median age was 51 years (IQR 45 to 59 years), 38% reported smoking, 50% used alcohol regularly, 62% were ECOG 0, and 26% had private health insurance. In the entire cohort, 203 (38%) patients were given GCSF. Among those treated with GCSF, 80 (39%) and 123 (61%) individuals received GCSF as primary and secondary prophylaxis, respectively. Overall, neutropenia was noted in 292 (56%) cases while neutropenic fever was experienced by 117 (22%) patients. When compared to those who did not use GCSF, patients who used GCSF experienced a lower rate of neutropenia (15 vs 49%, p<0.01) and a decreased incidence of neutropenic fever (7 vs 13%, p<0.01). In regression models, patients lacking extended medical coverage (35 vs 49%, p=0.02), poor performance status (31 vs 53%, p=0.03), and those who were evaluated at non-teaching institutions (24 vs 68% p<0.01) were less likely to receive GCSF. Patients seen at non-teaching institutions were also given primary GCSF prophylaxis less frequently (15 vs 57%, p<0.01) than those at teaching centers. Conclusions: GCSF prophylaxis was associated improved neutropenia-related outcomes. However, use of GCSF was low in this population-based cohort of BrCa patients, especially in specific marginalized subgroups.
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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 | 0.001 | 0.002 |
| 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".