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Record W1493842745 · doi:10.14288/hfjc.v2i1.15

Physical Activity during Breast Cancer Treatment

2010· article· en· W1493842745 on OpenAlexaff
Madeline Noble, Roni Jamnik

Bibliographic record

VenueOpen Collections · 2010
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsYork University
Fundersnot available
KeywordsMedicineCardiorespiratory fitnessAerobic exerciseBreast cancerExercise prescriptionPhysical therapyQuality of life (healthcare)Strength trainingCancerModalitiesPhysical medicine and rehabilitationInternal medicineNursing

Abstract

fetched live from OpenAlex

The increased breast cancer survival rate has directed cancer care toward developing interventions to improve quality of life. Physical activity has been identified as a valuable intervention that can help to manage symptoms and restore optimal functioning. Aerobic exercise programs can preserve or improve cardiorespiratory fitness and when combined with resistance training, conjointly improve muscular strength. Following certain treatment modalities, shoulder range of motion may be compromised and physical activity can help restore joint mobility. Exercise programs consisting of aerobic and resistance training result in improvements in various quality of life indices; patients experience reduced distress, enhanced well-being and improved self esteem. Cancer-related fatigue is one of the most common side effects associated with cancer treatment. It is not alleviated by rest or sleep, yet has been shown to be ameliorated by aerobic exercise. Weight gain often occurs in women receiving chemotherapy for breast cancer which is not only a source of distress but also additional risk for development of chronic illnesses. Combined aerobic and resistance training exercise programs have been successful in preventing weight gain when receiving chemotherapy. However, exercise prescription should be highly individualized to the patient and be recommended by a qualified exercise professional.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.317
Teacher spread0.299 · 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 designBench or experimental
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

Citations1
Published2010
Admission routes1
Has abstractyes

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