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Record W2897597212 · doi:10.1093/nop/npy035

Complementary and alternative medicine use by glioma patients in Switzerland

2018· article· en· W2897597212 on OpenAlexaff
Günter Eisele, Ulrich Roelcke, Katrin Conen, Fabienne Huber, Tobias Weiß, Silvia Höfer, Oliver Heese, Manfred Westphal, Caroline Hertler, Patrick Roth, Michael Weller

Bibliographic record

VenueNeuro-Oncology Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGliomaMedicineReimbursementDiseaseAlternative medicineFamily medicineHealth careCross-sectional studyInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: During the course of disease, most glioma patients learn that there is no cure for their tumor. It is therefore not uncommon for patients or caregivers to seek complementary and alternative medicine (CAM) treatments. Patterns of CAM use vary across the globe, but little is known about the type of, and motivation for, CAM use in most countries. METHODS: Here we conducted a cross-sectional survey of CAM use in patients harboring gliomas of World Health Organization (WHO) grades II to IV at 3 specialized neuro-oncology centers in Switzerland. RESULTS: Of 208 patients who returned the survey, approximately half reported having used or using CAM. CAM use was associated with younger age. Patients suffering from WHO grade II gliomas were less likely to indicate CAM use. The leading motivation for CAM use was to contribute actively to the treatment of the disease. CAM use was commonly not counseled or supervised by a health care professional. Cost and issues of reimbursement were not an important factor in the decision against or for CAM use. CONCLUSIONS: Physicians caring for glioma patients should be aware of and explore CAM use to better understand patients' attitudes toward their disease, to provide counseling, and to identify potential interactions of CAM with standard treatments for gliomas.

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)0.003
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 score0.666
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.054
GPT teacher head0.385
Teacher spread0.330 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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