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Record W4311162619 · doi:10.31189/2165-6193-11.4.146

Exercise Prescription for People With Metastatic Cancer of the Skeleton

2022· article· en· W4311162619 on OpenAlexaff
Robert U. Newton, Kirstin N. Lane, Nicolas H. Hart

Bibliographic record

VenueJournal of Clinical Exercise Physiology · 2022
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMedicinePsychological interventionHealth careDiseaseExercise prescriptionMedical prescriptionAlternative medicineAdverse effectPhysical therapyClinical trialMEDLINEIntensive care medicineNursingPathologyInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT Exercise is increasingly accepted as a therapy in the management of cancer, and is now described as a medicine, giving rise to a new discipline in clinical practice and research termed exercise oncology . Exercise medicine has been evaluated in clinical trials and implemented in patient care at all phases of disease and treatment trajectory. Advanced disease involving bone metastases presents considerable challenges in terms of patient assessment and exercise prescription. Over the past decade research evidence has accumulated attesting to the safety and efficacy of appropriately designed exercise medicine interventions. Combined with a need for well-developed guidelines, an expert consensus has been developed. Through a rigorous process the overarching recommendation was that exercise professionals should work with the patient and their health care team to balance the risk of adverse events due to participation in exercise therapy against the risk of more rapid patient decline through not exercising, as well as the potential loss of health benefits that could be realized through exercise. This is the basic tenet of health care and withholding or not offering a therapy that is likely to provide greater benefit than the potential risk it may cause for fear of that risk is untenable.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0010.000

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 head0.050
GPT teacher head0.384
Teacher spread0.334 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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