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Record W4385584151 · doi:10.14740/wjon1647

Assessing Adherence to Adjuvant Hormone Therapy in Breast Cancer Patients in Routine Clinical Practice

2023· article· en· W4385584151 on OpenAlexvenueno aff
Natalia Camejo, Cecilia Castillo, Clara Tambasco, Noelia Strazzarino, Nicolás Requena, Silvina Peraza, Anna Boronat, Guadalupe Herrera, Patricia Esperón, Mauricio Cuello, Gabriel Krygier

Bibliographic record

VenueWorld Journal of Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineLogistic regressionOdds ratioPolypharmacyBreast cancerConfidence intervalExact testAdjuvant therapyOncologyAdjuvantHormone therapyGynecologyCancer

Abstract

fetched live from OpenAlex

Background: Adjuvant hormone therapy (HT) in patients with hormone receptor-positive breast cancer (BC) increases overall survival (OS). A lack of adherence to adjuvant endocrine therapy is common, 31.0-73.0% of women discontinue endocrine treatment before 5 years. The aim of the study was to assess adherence to HT in routine clinical practice in patients assisted at the Clinical Oncology Department of the Hospital de Clinicas - Universidad de la Republica, Uruguay. Methods: Patients treated with HT for stage 0-III BC between 2017 and 2019 were included. The medication possession (MPR) rate was calculated using pharmacy records, and the Morisky-Green Scale was applied to assess adherence. Adherent patients were those with MPR ? 0.80 and who correctly answered the Morisky-Green treatment adherence questionnaire. The association of adherence with polypharmacy, treatment, and patient characteristics was assessed using simple logistic models. The associations between qualitative variables and adherence were assessed using simple logistic regression model or Fisher’s exact test. The association between quantitative variables and adherence was assessed using the Student’s t -test. The odds ratio (OR) for non-adherence to treatment and its 95% confidence interval were estimated. Results: Totally, 118 patients were included; 65.2% were treated with aromatase inhibitors (AIs), 36.0% presenting polypharmacy. The adherence rate at the end of 2 years was 81.0 %; and it was associated with age (P = 0.03, OR = 0.96 for non-adherence), with adherent and non-adherent patients having a mean age of 65.0 and 60.3 years, respectively; however, adherence was not associated with polypharmacy, territory of origin, marital status, living alone, level of education, occupation, or stage. The adherence profile was similar for both drugs, but homemakers and retired women showed greater adherence to AI. Conclusions: Adherence to HT was assessed in real life, with 19.0% of the patients not adhering to the treatment, despite the known benefit for OS, being a well-tolerated treatment, and being provided free of charge. Older patients were associated with being more adherent. The results show the need of the Pharmacy Service and Department of Clinical Oncology Medical Oncology combining efforts to develop coordinated strategies and interventions to increase adherence, given the impact that this may have on patients’ OS. World J Oncol. 2023;14(4):300-308 doi: https://doi.org/10.14740/wjon1647

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.136
GPT teacher head0.498
Teacher spread0.362 · 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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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