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Record W2118298189 · doi:10.1177/1049732306288541

The Quality of Life of Elderly Women Who Underwent Radiofrequency Ablation to Treat Breast Cancer

2006· article· en· W2118298189 on OpenAlexaff
Jillian Roberts, Lani Morden, Sheryl MacMath, Kendra Massie, Ivo A. Olivotto, Cathy Parker, Allen Hayashi

Bibliographic record

VenueQualitative Health Research · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsBC Cancer AgencyIsland HealthUniversity of Victoria
Fundersnot available
KeywordsQuality of life (healthcare)MedicineBreast cancerFeelingRadiofrequency ablationCancerQualitative researchCancer treatmentFamily medicineAblationNursingPsychologyInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

The purpose of this article is to explore the effects of radiofrequency ablation (RFA), an investigational treatment for breast cancer, on the quality of life of elderly women. For this descriptive phenomenological study, the authors interviewed 12 White women (aged 60-81 years) 4 months to 1 year after treatment and analyzed these interviews for common themes. They asked questions regarding the lived experience of RFA treatment and its effects on quality of life. Analyses focused on the effects of deciding to have the RFA treatment and the treatment itself. They found quality of life improved because the women felt empowered by (a) their decision to have the procedure, (b) knowing that the procedure might kill the tumor, (c) and feeling that they were contributing to cancer research. The level of support received from the medical team, family and friends, and other cancer survivors also improved participant quality of life.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.164
GPT teacher head0.521
Teacher spread0.358 · 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 designQualitative
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

Citations19
Published2006
Admission routes1
Has abstractyes

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