A multicenter study comparing granulocyte-colony stimulating factors to antibiotics for primary prophylaxis of taxotere/cyclophosphamide-induced febrile neutropenia in patients with early-stage breast cancer.
Bibliographic record
Abstract
7001 Background: Docetaxel-cyclophosphamide (TC) adjuvant chemotherapy is commonly used in patients with early stage breast cancer (EBC). Due to the risk of febrile neutropenia (FN) with TC, primary prophylaxis with either ciprofloxacin (cipro) or granulocyte-colony stimulating factors (G-CSF) is recommended. Despite significant differences in costs (7-120 $US/course [cipro] vs. 2100-7000 $US/dose [G-CSF]) and toxicity profiles, optimal primary FN prophylaxis is unknown. We performed a pragmatic randomised trial comparing the superiority of G-CSF to cipro. Methods: EBC patients receiving TC chemo were randomized to receive cipro or G-CSF as primary FN prophylaxis. The primary outcome is a composite of either treatment-related hospitalisations or FN. Secondary outcomes included: chemo dose reductions, delays, discontinuations and incidence of C. difficile infections. Primary analysis was performed with the intention to treat (ITT) population. Results: 455 eligible patients were randomized to cipro (227) or G-CSF (228). 37/227 (16.3%) patients on cipro had a hospitalization, compared with 25/228 (11.0%) on G-CSF (Fisher’s exact test p-value=0.10). Relative risk (RR) of hospitalization for patients on G-CSF:0.68, 95%CI=0.42 to 1.09. Patients on cipro were statistically significantly more likely to be hospitalized for FN (30/227, 13.2%) vs 9/228 (4.0%) patients on G-CSF(p<0.001). RR of developing FN and being hospitalized for patients on G-CSF: 0.44, 95%CI=0.26 to 0.76. There was no significant difference between groups for chemo dose reductions, delays, and C. difficile rates. Twenty patients on cipro (8.8%) and 9 on G-CSF (3.9%) discontinued chemo early (p=0.036). RR of discontinuing chemo: 0.43, 95%CI=0.19 to 0.96. Conclusion: G-CSF was superior to cipro at reducing FN. While a trend towards reduced hospitalizations was also observed with G-CSF, it did not attain statistical significance. However, as 18 patients would need to be treated with G-CSF to prevent one hospitalization compared to cipro, this would suggest a cost of over $100000 $US to prevent a hospitalization. A formal cost-effectiveness analysis will be performed. Clinical trial information: NCT02173262, NCT02816112 .
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".