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Record W2140951803 · doi:10.5737/23688076254384395

Towards an optimal multidisciplinary approach to breast cancer treatment for older women

2015· review· en· W2140951803 on OpenAlexaffvenue
Nemica Thavarajah, Ines B. Menjak, Maureen Trudeau, Rajin Mehta, Frances W. Wright, Angela Leahey, Janet Ellis, Damian Gallagher, Jennifer Moore, Bonnie Bristow, Noreen Kay, Ewa Szumacher

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2015
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsBreast cancerMedicineMultidisciplinary approachGeriatric oncologyGeriatricsMEDLINEFamily medicinePopulationPalliative careRadiation therapyCancerIntensive care medicineOncologyGerontologyNursingInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

The treatment of breast cancer presents specifc concerns that are unique to the needs of older female patients. While treatment of early breast cancer does not vary greatly with age, the optimal management of older women with breast cancer often requires complex interdisciplinary supportive care due to multiple comorbidities. This article reviews optimal approaches to breast cancer in women 65 years and older from an interdisciplinary perspective. A literature review was conducted using MEDLINE and EMBASE, choosing articles concentrated on the management of older breast cancer patients from the point of view of several disciplines, including geriatrics, radiation oncology, medical oncology, surgical oncology, psychooncology, palliative care, nursing, and social work. This patient population requires interprofessional collaboration from the time of diagnosis, throughout treatment and into the recovery period. Thus, we recommend an interdisciplinary program dedicated to the treat ment of older women with breast cancer to optimize their cancer care.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.095
GPT teacher head0.427
Teacher spread0.331 · 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.

Study designNot applicable
Domainnot available
GenreReview

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
Published2015
Admission routes2
Has abstractyes

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