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Record W4254185646 · doi:10.1002/cncr.25249

Reply to Comparison of hospitalization risk and associated costs among patients receiving sargramostim, filgrastim, and pegfilgrastim for chemotherapy‐induced neutropenia

2010· article· en· W4254185646 on OpenAlexaffabout
Mark Heaney, Edmond L. Toy, Francis Vekeman

Bibliographic record

VenueCancer · 2010
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsPegfilgrastimFilgrastimMedicineNeutropeniaFebrile neutropeniaGranulocyte colony-stimulating factorChemotherapyIncidence (geometry)Intensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

We appreciate the concerns raised by Weycker and Barron regarding our study of the effects of sargramostim, filgrastim, and pegfilgrastim on hospitalization risks associated with chemotherapy-induced neutropenia,1 but believe that their criticisms do not call into question the study's conclusions. We agree that the risk of febrile neutropenia (FN) varies across multiple chemotherapy cycles, so we focused our study design on the use of colony-stimulating factors (CSFs) during the first few chemotherapy cycles, thereby capturing the period with the highest incident risk of FN. Our methodology also took into account the use of CSFs for treatment versus prophylaxis because the adjusted hospitalization incidence rates controlled for the occurrence of a neutropenia diagnosis on the date of CSF initiation, reflecting CSF use for treatment rather than prophylaxis. The results found that pegfilgrastim indeed appears to be used more frequently for prophylaxis than sargramostim and filgrastim, and this potential confounding factor was accounted for in the regression model. Moreover, it is unlikely from a clinical perspective that sargramostim and filgrastim would be used differentially for treatment versus prophylaxis, thereby reducing the likelihood of biased estimates. With regard to the duration of follow-up across the CSFs, Weycker and Baron appear to imply that the longer observation period penalizes pegfilgrastim (ie, assigns a higher incidence rate of hospitalization to pegfilgrastim). We believe that the longer therapeutic duration of pegfilgrastim compared with sargramostim and filgrastim necessitates a longer observation period and chose 19 days as the average real-life interval between pegfilgrastim administrations. Moreover, the use of patient-years of observation as the denominator for the incidence rate accounts for the longer therapeutic duration. The matched cohort design was challenged because it excludes the majority of filgrastim and pegfilgrastim patients. Although there are many ways to control for potential confounders, matched cohorts are a well-established research design.2, 3 Indeed, when considering all, rather than matched pairs of CSF patients in the claims data, we found that filgrastim and pegfilgrastim patients tend to be younger (unpublished data). Matching thus reduced the likelihood of confounding due to differences in the age and gender distribution and permitted a better comparison. We agree that further study of this important topic is warranted, but we believe that our analysis and methodology appropriately considered real-world practice and experience. Mark L. Heaney MD, PhD*, Edmond L. Toy PhD , Francis Vekeman MA , * Department of Medicine Memorial Sloan-Kettering Cancer Center New York, New York, Analysis Group, Inc Lakewood, Colorado, Analysis Group, Inc Montreal, Quebec, Canada.

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)0.009
metaresearch head score (Gemma)0.067
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.067
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0140.013
Insufficient payload (model declined to judge)0.0070.003

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 head0.012
GPT teacher head0.307
Teacher spread0.294 · 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
GenreCommentary

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

Citations0
Published2010
Admission routes2
Has abstractyes

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