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Record W2593911674 · doi:10.1177/1179554917691266

Understanding Women’s Choice of Mastectomy Versus Breast Conserving Therapy in Early-Stage Breast Cancer

2017· article· en· W2593911674 on OpenAlexafffundabout
Jeffrey Gu, Gary Groot, Lorraine Holtslander, Rachel Engler‐Stringer

Bibliographic record

VenueClinical Medicine Insights Oncology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsUniversity of Saskatchewan
FundersSaskatchewan Cancer Agency
KeywordsBreast cancerMastectomyWorryThematic analysisQualitative researchMedicinePsychologyCancerInternal medicineSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify factors that influence Saskatchewan women's choice between breast conserving therapy (BCT) and mastectomy in early-stage breast cancer (ESBC) and to compare and contrast underlying reasons behind choice of BCT versus mastectomy. METHODS: Interpretive description methods guided this practice-based qualitative study. Data were analyzed using thematic analysis and presented in thematic maps. RESULTS: Women who chose mastectomy described 1 of the 3 main themes: worry about cancer recurrence, perceived consequences of BCT treatment, or breast-tumor size perception. In contrast, women chose BCT because of 3 different themes: mastectomy being too radical, surgeon influence, and feminine identity. CONCLUSIONS: Although individual reasons for choosing mastectomy versus BCT have been discussed in the literature before, different rationale underlying each choice has not been previously described. These results are novel in identifying interdependent subthemes and secondary reasons for each choice. This is important for increased understanding of factors influencing a complicated decision-making process.

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)0.010
metaresearch head score (Gemma)0.019
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.220
GPT teacher head0.442
Teacher spread0.222 · 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".

Quick stats

Citations38
Published2017
Admission routes3
Has abstractyes

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