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Record W4311613428 · doi:10.3390/cancers14246197

Real-World Use of Granulocyte-Colony Stimulating Factor in Patients with Breast Cancer from Alberta, Canada

2022· article· en· W4311613428 on OpenAlexaffabout
Philip Q. Ding, Brandt J. Newcomer, Winson Y. Cheung

Bibliographic record

VenueCancers · 2022
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsApotex (Canada)University of CalgaryUniversity of Alberta
Fundersnot available
KeywordsFilgrastimMedicineGranulocyte colony-stimulating factorPegfilgrastimBreast cancerNeutropeniaInternal medicineChemotherapyOncologyBiosimilarCancerSurgery

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.251
Teacher spread0.236 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes2
Has abstractyes

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