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Record W3136144563 · doi:10.1097/hnp.0000000000000447

The Use of Complementary and Alternative Medicine and Quality of Life in Patients With Hip and Knee Osteoarthritis

2021· article· en· W3136144563 on OpenAlexaboutno aff
Çiğdem Canbolat Seyman, Hayriye Ünlü

Bibliographic record

VenueHolistic Nursing Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACMedicineOsteoarthritisPhysical therapyQuality of life (healthcare)Logistic regressionAlternative medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

The use of complementary and alternative medicine (CAM) practices was common among patients with osteoarthritis (OA) since the patients experienced severe problems. The aim of this study was to determine the prevalence of CAM use and quality of life in pre-arthroplasty patients. This study was designed as a descriptive, consecutive survey of pre-arthroplasty patients due to hip and knee OA. Data were collected by the Personal Information Form, Western Ontario and McMaster Universities (WOMAC) Index, and EQ-5D-5L quality-of-life scale. Logistic regression was used to determine the risk factors of CAM usage; 74.4% of the patients used CAM methods. The most frequently used methods of CAM were biologically based herbal therapies. All patients indicated that they did not disclose CAM methods they used to their physicians. The median EQ-5D-5L index value of the patients was 0.08 and the median WOMAC score was 96.8. Furthermore, patients with right knee OA were found to have a higher risk of using CAM. This study demonstrated that communication between patients and health care professionals is generally poor, and there is an urgent need to develop patient education to minimize the risks and maximize the benefits of using CAM.

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)0.002
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 score0.487
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.112
GPT teacher head0.360
Teacher spread0.248 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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