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Differential efficacy of antidepressants in the treatment of hormone mediated depression in patients with ER+ breast cancer.

2023· article· en· W4379281575 on OpenAlexaboutno aff
Martha Kato, Constanza Martínez, Maria Currier, Muni Rubens

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerEscitalopramInternal medicineDepression (economics)OncologyMontreal Cognitive AssessmentBeck Depression InventoryAnxietyCancerPsychiatryAntidepressantDiseaseDementia

Abstract

fetched live from OpenAlex

e24181 Background: Tamoxifen (TAM) and aromatase inhibitors (AI) are associated with a high side-effect burden including depression and cognitive deficits which compromise quality of life and adherence to treatment. The aims of the study were to determine the efficacy of antidepressants (AD) with hormone mediated depression in ER+ breast cancer patients and to determine if any clinical variables predicted successful AD treatment response. Methods: A retrospective chart review of consecutive referrals to psycho-oncology of breast cancer patients on TAM or AI with new onset depression was conducted. Demographic variables, cancer stage and treatment, hormonal treatment, psychiatric history and AD treatment were collected. Assessments for depression, anxiety and cognitive disorders included clinical interviews, patient-rated scales [Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI)] and a clinician-rated scale [Montreal Cognitive Assessment (MoCA)]. These measures were done at baseline (before starting an AD) and follow-up to determine AD treatment response. Successful AD treatment is defined as ≥50% reduction in baseline BDI score, while remission of depression is defined as a BDI score < 8. Statistical analysis comparing clinical variables between AD responders and non-responders included Fischer’s exact test or chi-square, one-way ANOVA, t-test, and binary logistic regression to determine which variables predicted successful AD treatment response. Results: A sample of 40 ER+ breast cancer patients (mean age 55 years; 39 females, 1 male; 26 Hispanic, 14 non-Hispanic) included 18 on TAM and 22 on AI. AD included venlafaxine (N = 18), escitalopram (N = 9), bupropion (N = 4), mirtazapine (N = 4), sertraline (N = 3) and duloxetine (N = 2). Over 72% (29/40) of patients successfully responded to AD with a complete depression remission rate of 50% (20/40). Venlafaxine at a mean dose of 150 mg was significantly more effective (p = .013) in treating hormone-mediated depression when compared to selective serotonin reuptake inhibitors (SSRIs) and other AD (bupropion, mirtazapine). Specifically, 94% (17/18) of patients on venlafaxine successfully responded. Other variables such as cancer stage and treatment, demographics, psychiatric history, and specific hormonal therapy did not predict AD treatment response. Conclusions: Venlafaxine 150 mg daily yielded significantly higher rates of AD response and complete remission of depression in ER+ breast cancer patients with TAM-induced and AI-induced major depression. Venlafaxine was significantly more effective than SSRIs and atypical AD. This high rate of response is likely due to dual serotonin and norepinephrine reuptake inhibition achieved at 150 mg dosing. Venlafaxine can be safely administered with TAM.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.064
GPT teacher head0.431
Teacher spread0.367 · 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 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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