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Record W3026258687 · doi:10.1188/20.cjon.e34-e42

Breast Cancer Survivor Symptoms: A Comparison of Physicians’ Consultation Records and Nurse-Led Survivorship Care Plans

2020· article· en· W3026258687 on OpenAlexaff
Christina Kozul, Lesley Stafford, Ruth Little, Chad Bousman, Allan Park, Kerry Shanahan, G. Bruce Mann

Bibliographic record

VenueClinical journal of oncology nursing · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineSurvivorship curveBreast cancerCancer survivorshipCancerQuality of life (healthcare)Stage (stratigraphy)Nurse practitionersOncology nursingCancer treatmentHealth careOncologyFamily medicineIntensive care medicineInternal medicineNursingNurse education

Abstract

fetched live from OpenAlex

BACKGROUND: Survivorship care plans (SCPs) have been used to address ongoing health problems associated with the diagnosis and treatment of early-stage breast cancer. OBJECTIVES: The aim of this article was to determine whether nurse-led consultations using SCPs, as compared with a standard medical consultation, identify more side effects and supportive care needs and lead to appropriate referral patterns. METHODS: The study audited 160 retrospective medical clinic and nursing SCP records in a sample of patients receiving treatment for early-stage breast cancer at a tertiary-level breast service in Australia. FINDINGS: Breast care nurses (BCNs) undertaking SCPs at a nurse-led consultation were significantly more likely than physicians to record symptoms related to menopausal/hormonal therapy, psychosocial/mental health, lifestyle, bone health, and sexuality. BCNs were also significantly more likely to refer patients for concerns related to psychosocial/mental health, lifestyle, and sexuality.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.419
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.063
GPT teacher head0.436
Teacher spread0.373 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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