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Abstract P3-03-09: Assessment of Breast Cancer Chemotherapy Dose Reduction in an Integrated Healthcare Delivery System

2023· article· en· W4322774777 on OpenAlexaff
Elizabeth D. Kantor, Kelli O’Connell, Isaac J. Ergas, Emily Valice, Janise M. Roh, Jenna Bhimani, Narre Heon, Jennifer J. Griggs, Jean Lee, Erin J. Aiello Bowles, Donna R. Rivera, Tatjana Kolevska, Elisa V. Bandera, Lawrence H. Kushi

Bibliographic record

VenueCancer Research · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsMedicineDosingBreast cancerBody surface areaCancerChemotherapyRegimenCyclophosphamideOncologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background: Most cytotoxic drugs are dosed according to body surface area (BSA), yet not all patients receive the full BSA-determined dose. Prior work suggests that dose reduction may occur more frequently in obese patients, likely due to concern about inducing toxicity at high doses. Other factors, such as race/ethnicity, have been suggested to be associated with dosing, yet the factors associated with dose reduction remain poorly understood, with little known about dosing patterns in integrated healthcare delivery systems and how such patterns have changed over time. Methods: We examined chemotherapy dosing in 452 women diagnosed with stage I-IIIA primary breast cancer at Kaiser Permanente Northern California. All study participants were a part of the Pathways Study, diagnosed between 2006-2013, and treated with the common breast cancer regimen, ACT (cyclophosphamide and doxorubicin, followed by paclitaxel). We explored the association between obesity and dose reduction, and further explored other factors in relation to dose reduction, including various sociodemographic characteristics, tumor characteristics, and comorbidities. We assessed dosing using first cycle dose proportion (< 90% expected dose) and average relative dose intensity (ARDI, a metric of dose intensity over the entire course of chemotherapy). Results: Overall, 8% of women received a dose reduction >10% in the first cycle of chemotherapy and 21.2% of patients had an ARDI < 90%. Obesity was a strong predictor of dose reduction, both in the first cycle and across all cycles. In the first cycle, 21.9% of severely obese patients (body mass index, BMI: 35+ kg/m2) were dose reduced, whereas no normal weight patients (BMI: 18.5-< 25 kg/m2) experienced a first cycle dose reduction. Across all cycles, 38.4% of severely obese women had an ARDI < 90%, as compared 12.8% of normal weight women. In logistic regression models adjusted for age, race/ethnicity, and white blood cell count, obese women had 4.1-fold higher odds of receiving a dose reduction of 10% or more over the course of chemotherapy than their normal weight counterparts (95% CI: 1.9, 8.9; p-trend: 0.006). Increasing age was positively associated with dose reduction across the course of chemotherapy, as was the presence of comorbidity. Importantly, dose reduction was less common in later calendar years. Sensitivity analyses revealed that the positive association between obesity and dosing was robust to further adjustment for these other significantly associated factors. Impact: These results offer insight on factors associated with variation in chemotherapy dosing for a common breast cancer treatment regimen. Larger studies are required to evaluate relevance of these findings to other treatment regimens. Further work will be needed to determine whether dose reductions impact breast cancer outcomes. Citation Format: Elizabeth D. Kantor, Kelli O’Connell, Isaac J. Ergas, Emily Valice, Janise M. Roh, Jenna Bhimani, Narre Heon, Jennifer J. Griggs, Jean Lee, Erin J. Bowles, Donna R. Rivera, Tatjana Kolevska, Elisa Bandera, Lawrence H. Kushi. Assessment of Breast Cancer Chemotherapy Dose Reduction in an Integrated Healthcare Delivery System [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-09.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.129
GPT teacher head0.497
Teacher spread0.368 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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