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Record W2326855513 · doi:10.7759/cureus.555

A Single Institution Consensus on the Use of Sequential or Concurrent Hormonal Therapy for Breast Cancer Patients Receiving Radiation Therapy

2016· article· en· W2326855513 on OpenAlexaff
Matthew J. Cecchini, Edward Yu, Brian Yaremko, Gabriel Boldt, Kylea Potvin, Tracy Sexton, David D’Souza, Muriel Brackstone, Michael Lock

Bibliographic record

VenueCureus · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineHormonal therapyBreast cancerTamoxifenRadiation therapyHormone therapyAdverse effectIntensive care medicineOncologyCancerInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: For hormone-sensitive breast cancers, treatment with breast-conserving surgery, tamoxifen, or aromatase inhibitors, along with adjuvant radiation, is the mainstay of therapy. The ideal timing of hormonal and radiation treatment is not well defined, and there is a significant degree of practice variability between concurrent and sequential treatment regimes. This variability can cause confusion amongst the clinical team resulting in contradictory recommendations, loss of patient trust, and the potential for missed initiation of hormonal therapy. METHODS: To address this question, a systematic review of the literature was conducted and presented to the breast cancer multidisciplinary team at the London Regional Cancer Center. A three-round modified Delphi method was used to obtain a consensus on a series of a priori determined statements. RESULTS: With the currently available evidence, the consensus was that hormonal therapy should be given sequentially after radiation. This will limit potential overlapping adverse effects between hormonal therapy and radiation that may decrease completion of treatment. The sequential approach has not been associated with any harm in clinical outcomes, and there is some suggestion of increased toxicity with concurrent use. However, in patients at high risk of distant recurrence, they felt it would be reasonable to consider concurrent treatment to avoid any delay in therapy. CONCLUSION: The consensus of our institution to utilize a sequential approach will standardize the treatment decisions and reduce the risk of failing to initiate hormonal therapy. Despite the lack of level 1 evidence, the Delphi methodology did provide a high level of confidence for our group to choose the sequential approach. The consensus was developed after a review of the literature revealed that there was no clear superiority of one schedule over the other and evidence that concurrent treatment may increase adverse events.

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)0.313
metaresearch head score (Gemma)0.330
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.847

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3130.330
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0060.004
Science and technology studies0.0060.006
Scholarly communication0.0060.005
Open science0.0050.013
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.075
GPT teacher head0.300
Teacher spread0.225 · 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.

Study designNot applicable
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

Citations1
Published2016
Admission routes1
Has abstractyes

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