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Record W2056583840 · doi:10.1002/pon.1406

Evaluation of a cancer exercise program: patient and physician beliefs

2008· article· en· W2056583840 on OpenAlexaff
Carmen Peeters, Alistair Stewart, Roanne Segal, E. Wouterloot, Christopher G. Scott, Tim Aubry

Bibliographic record

VenuePsycho-Oncology · 2008
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsOttawa HospitalThe Sisters of Charity of OttawaUniversity of Ottawa
Fundersnot available
KeywordsCancerMedicinePhysical therapyFamily medicineMedical educationInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Participation in an exercise intervention during cancer treatment diminishes the side effects associated with cancer therapies, although such benefits vary according to the disease and the patient characteristics. A structured exercise program providing an individualized fitness program tailored to the patients' illness, treatment, and fitness level would address this variability. However, the need, desired components, and anticipated barriers of such a program have not been systematically explored from either the point of view of cancer patients or treating oncologists. METHODS: Sixty-six cancer patients and 18 medical and radiation oncologists were surveyed on the above variables. RESULTS: Cancer patients and oncologists alike perceived a need for a structured exercise program during and after medical treatment for cancer. Among cancer patients, the most commonly preferred feature was access to consultation with an exercise specialist who could take into account the patient's previous exercise and medical history. Over a third of patients reported interest in a hospital-based fitness program. Oncologists were in favor of appropriate supervision of patients during exercise, and noted insufficient time to discuss exercise in their practice. Respondents noted time and parking as barriers to participation. CONCLUSION: Overall, results support the need for a supervised exercise program during active treatment for cancer and highlight the desired features of such a program.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.350

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 studies0.0000.000
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 head0.056
GPT teacher head0.399
Teacher spread0.343 · 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 designOther design
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

Citations35
Published2008
Admission routes1
Has abstractyes

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