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Abstract P6-07-06: Primary prophylaxis of febrile neutropenia during adjuvant docetaxel and cyclophosphamide (TC) chemotherapy for breast cancer

2013· article· en· W2093590450 on OpenAlexaffabout
JL Yu, Michael Kurin, Mark Pasetka, Alexander Kiss, Kuen Chan, SS Sridhar, Ellen Warner

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsPrincess Margaret Cancer CentreInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineFebrile neutropeniaDocetaxelInternal medicineCyclophosphamideBreast cancerRetrospective cohort studyNeutropeniaOncologyCohortChemotherapyCancerSurgery

Abstract

fetched live from OpenAlex

Abstract Background: The combination of docetaxel and cyclophosphamide (TC) for adjuvant treatment of early stage breast cancer improves overall survival compared with doxorubicin and cyclophosphamide (AC) (Jones et al., 2006). Although cardiotoxicity is avoided with TC, the risk of febrile neutropenia (FN) is higher. For TC, reported rates of FN without prophylactic granulocyte colony-stimulating factor (G-CSF) range from 5% in the phase III trial to as high as 46% in retrospective chart reviews. G-CSF is not covered by our provincial cancer funding agency for primary prophylaxis of FN with TC chemotherapy, however it is often prescribed for patients with private insurance. Our aims were twofold: i) to determine the incidence of FN with TC chemotherapy with and without prophylactic G-CSF or antibiotics in two Ontario comprehensive cancer centres, and ii) to evaluate the cost-effectiveness of primary prophylaxis with G-CSF vs. antibiotics. Methods: Patients who received adjuvant TC chemotherapy between January 1, 2008 and December 31, 2012 were identified through pharmacy databases. Electronic charts were retrospectively reviewed to extract patient characteristics, treatment details including G-CSF and antibiotic use, as well as incidence of FN and duration of hospitalization. A Markov model comparing primary G-CSF prophylaxis, primary antibiotic prophylaxis and secondary G-CSF prophylaxis was constructed to compare the cost-effectiveness of these strategies over a four cycle time horizon. Costs were based on resource utilization from this retrospective cohort and supplemented by the published literature, adjusted to 2012 Canadian dollars. The model took the perspective of the third party payer. Both one-way and probabilistic sensitivity analyses were performed. Results: 340 patients were treated with TC over the study period. Of the 73 (21%) who did not receive any primary prophylaxis with G-CSF or antibiotics, 23 (32%) developed FN requiring hospitalization and treatment with intravenous antibiotics. However, only 2 of the 192 patients (1%; P <0.0001) who received primary G-CSF prophylaxis (funded by the patient or a third party payer), and 6 of the 53 patients (11%; P <0.01) who received primary antibiotic prophylaxis (97% receiving ciprofloxacin) developed FN. Age ≥65 was a significant risk factor for FN in the absence of G-CSF (56% vs. 25%, P = 0.02). The results of the cost-effectiveness analysis will be presented at the meeting. Conclusions: The FN rate associated with TC chemotherapy without primary prophylaxis exceeds 30% but may be reduced with prophylactic antibiotics or G-CSF. Unless prophylactic antibiotics are substantially more cost-effective than prophylactic G-CSF for TC chemotherapy in a particular region or country, primary prophylactic G-CSF should be funded, given its greater effectiveness than antibiotics and the global need to minimize the emergence of antibiotic resistance. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P6-07-06.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.229
Threshold uncertainty score0.454

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.031
GPT teacher head0.351
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2013
Admission routes2
Has abstractyes

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