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Record W6995600331

Pain Management Strategies in Patients with Knee Osteoarthritis and Hypertension: Use, and Differences in Pain and Arthritis Pain Self-Efficacy

2022· article· en· W6995600331 on OpenAlexaboutno aff

Bibliographic record

VenueD-Scholarship@Pitt (University of Pittsburgh) · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Production and Characterization
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisPain managementKnee painArthritisChronic painQuality of life (healthcare)Pain catastrophizingRandomized controlled trial
DOInot available

Abstract

fetched live from OpenAlex

Background: Chronic pain caused by knee osteoarthritis has a negative impact on patients’ quality of life. The prevalence of hypertension is high among patients with knee osteoarthritis, and usage of pain medications can increase patients’ blood pressure. Purpose: 1) Describe characteristics of pain and non pharmacological pain management strategies used by participants with knee osteoarthritis and hypertension in daily life; 2) Categorize pain management strategies and assess frequency and patterns of strategies patients used; and 3) Examine the effectiveness of pain management strategies on pain, and their relationship with pain self-efficacy. Method: This secondary analysis of data from a randomized controlled trial used qualitative and quantitative methods to address the aims. Seventy individuals from the 6-month intervention arm were included in this study. Qualitative data for Aims 1 and 2 were collected by semi-structured interview. Quantitative data for Aim 3 included participants’ knee pain, bodily pain, and pain self-efficacy, measured by Western Ontario and McMaster Universities Osteoarthritis Index, Short Form-36v2 Bodily Pain subscale, and Arthritis Pain Self-efficacy subscale, respectively, at baseline, immediate post-intervention, and 6 months post-intervention. Constant comparative and content analyses were used in Aims 1 and 2, respectively, to describe and summarize pain and pain management strategies that participants used. Linear mixed modeling was used in Aim 3 to assess differences in pain and pain self-efficacy for pain management strategies over time. Results: On average, participants employed five pain management strategies. The most commonly used strategies were practicing physical self-care activities, performing psychological self-care activities, being active, changing position, and avoiding overuse. Pain management strategies were categorized into treatment strategies only and both preventative and treatment strategies. Participants who only used treatment strategies reported significantly lower bodily pain (b=-7.94, p=.017) compared with participants who used both preventative and treatment strategies. A mediating effect of self-efficacy on the association between pain management strategies and pain was not found. Conclusion: Participants used multiple pain management strategies to control pain, and treatment strategies were favored, which health care providers can recommend to patients. Health care providers can suggest preventative strategies that are evidenced-based and that patients find effective to control their pain.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.170
Teacher spread0.161 · 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

Explore more

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