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Abstract P6-07-07: Febrile neutropenia primary prophylaxis with granulocyte-colony stimulating factors (G-CSF) in breast cancer

2013· article· en· W2021320698 on OpenAlexaff
T. Younis, Daniel Rayson, Chris Skedgel

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsMedicineFebrile neutropeniaGranulocyte colony-stimulating factorBreast cancerInternal medicineChemotherapyAdjuvantNeutropeniaSecondary prophylaxisOncologyCancerIntensive care medicine

Abstract

fetched live from OpenAlex

Abstract Background: Febrile neutropenia (FN) during adjuvant chemotherapy is associated with significant morbidity, mortality risk and incremental costs. It could also lead to chemotherapy dose reductions and worse cancer outcomes. Patients who develop FN are often prescribed secondary G-CSF prophylaxis with subsequent chemotherapy cycles to decrease the risk of further episodes of infection. Practice guidelines also recommend primary G-CSF prophylaxis for: i) patients treated with chemotherapeutic regimens associated with FN risks > 20% and ii) patients treated with regimens associated with 10-20% FN risk in the presence of other patient-related factors that further increase the risk of FN. The adoption of primary G-CSF prophylaxis in clinical practice however depend on the “value for money” associated with G-CSF prophylaxis at various FN risks, where incremental cost-utility values below $100,000 per quality adjusted life year (QALY) gains are generally considered to be cost-effective. Aim: To examine the “value for money” associated with primary and secondary G-CSF prophylaxis strategies, compared with a no G-CSF strategy, for adjuvant chemotherapy in breast cancer at varying FN risks. Methods: The incremental costs and QALYs associated with G-CSF prophylaxis (primary or secondary) were examined through a decision analysis framework that incorporated i) upfront costs of G-CSF treatment (primary or secondary); ii) varying rates of baseline FN risks and iii) downstream costs and QALY gains associated with adjuvant chemotherapy based on chemotherapy dose levels (0, -1, and -2) and G-CSF prophylaxis. The primary analysis involved adjuvant TC (taxotere & cyclophosphamide) chemotherapy regimen delivered every three weeks for four cycles. Probabilities and utilities were derived from the literature, and treatment costs were based on local resources. The robustness of the model to plausible ranges of uncertainty around key parameters / assumptions was examined in sensitivity analyses. Results: Primary G-CSF prophylaxis was a cost-effective strategy compared with secondary G-CSF prophylaxis in the base case scenario. The “value for money” associated with primary G-CSF prophylaxis however was dependant on the baseline FN risk without G-CSF, and assumptions around the impact of chemotherapy dose reductions on breast cancer relapse and mortality. Two way sensitivity analyses illustrated plausible combinations of baseline FN risks and detrimental impact of reduced chemotherapy dose associated with favorable value for money. Conclusions: Primary G-CSF prophylaxis overall appears to be a cost-effective strategy for patients at high FN risk. The FN threshold at which primary G-CSF is associated with good value for money is however dependent on the potential detrimental impact of reduced chemotherapy dose on breast cancer outcomes. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P6-07-07.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.052
GPT teacher head0.366
Teacher spread0.313 · 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 routes1
Has abstractyes

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