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Record W2077951536 · doi:10.1002/cncr.27465

A prospective model of care for breast cancer rehabilitation: Bone health and arthralgias

2012· review· en· W2077951536 on OpenAlexaff
Kerri M. Winters‐Stone, Anna L. Schwartz, Sandra C. Hayes, Carol J. Fabian, Kristin L. Campbell

Bibliographic record

VenueCancer · 2012
Typereview
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineBreast cancerOsteoporosisRehabilitationPhysical therapyEtiologyPsychological interventionCancerIntensive care medicineQuality of life (healthcare)Internal medicineNursing

Abstract

fetched live from OpenAlex

Musculoskeletal health can be compromised by breast cancer treatment. In particular, bone loss and arthralgias are prevalent side effects experienced by women treated with chemotherapy and/or adjuvant endocrine therapy. Bone loss leads to osteoporosis and related fractures, while arthralgias threaten quality of life and compliance to treatment. Because the processes that lead to these musculoskeletal problems are initiated when treatment begins, early identification of women who may be at higher risk of developing problems, routine monitoring of bone density and pain at certain stages of treatment, and prudent application of therapeutic interventions are key to preventing and/or minimizing musculoskeletal sequelae. Exercise may be a particularly suitable intervention strategy because of its potential to address a number of impairments; it may slow bone loss, appears to reduce joint pain in noncancer conditions, and improves other breast cancer outcomes. Research efforts continue in the areas of etiology, measurement, and treatment of bone loss and arthralgias. The purpose of this review is to provide an overview of the current knowledge on the management and treatment of bone loss and arthralgias in breast cancer survivors and to present a framework for rehabilitation care to preserve musculoskeletal health in women treated for breast cancer.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.921
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.066
GPT teacher head0.422
Teacher spread0.356 · 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 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

Citations45
Published2012
Admission routes1
Has abstractyes

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