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Record W2149596222 · doi:10.1177/1534735410395136

Use of Complementary and Alternative Medicine by Cancer Patients at a Montreal Hospital

2011· article· en· W2149596222 on OpenAlexaffabout
Maida Sewitch, Mark J. Yaffe⃰, Jenny Maisonneuve, Jaroslav F. Prchal, Antonio Ciampi

Bibliographic record

VenueIntegrative Cancer Therapies · 2011
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsSt Mary's Hospital CentreMcGill University
Fundersnot available
KeywordsMedicineFamily medicineCancerAlternative medicineColorectal cancerFocus groupReferralHealth carePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess feasibility of methods for a future study of complementary and alternative medicine (CAM) use by cancer patients treated in conventional health care settings. METHODS: Patients aged 18 years and older, fluent in English or French, and diagnosed with cancer from St. Mary's Hospital Center, Montreal, Canada participated. Feasibility was measured by the rates of participation and CAM use in the past 1 and 12 months. Following the survey, one patient focus group was held to better understand cancer patient perspectives on discussions of CAM that occur or not with their family physicians. RESULTS: Of 103 patients approached, 100 (97.1%; 77% female, 87% white) participated. Overall, 86% and 91% of respondents used at least one CAM in the past 1 and 12 months, respectively. More patients with breast compared with colorectal and other cancers (90.2%, 86.2%, and 80%, respectively) used CAM in the previous year. In the past 1 and 12 months, natural health products were used by 70% and 80% of respondents, respectively; mind-body therapies by 61% and 64%, respectively, and CAM practitioners by 11% and 29%, respectively. More than 98% of patients used CAM to improve quality of life and 68% disclosed CAM use to their physicians. Four of 5 focus group participants used CAM. Patient-physician CAM discussions varied from receiving a CAM referral to complete dismissal of the topic. CONCLUSION: Recruitment methods were well accepted but a sampling strategy stratified by sex and ethnicity will ensure sufficient representation by males and non-whites. Whereas disclosure of natural health products use is occurring, informative CAM discussion is not.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.331
Teacher spread0.258 · 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 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

Citations28
Published2011
Admission routes2
Has abstractyes

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