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Record W3217002048 · doi:10.3138/ptc-2020-0093

Factors Associated with Intermittent, Constant, and Mixed Pain in People with Knee Osteoarthritis

2021· article· en· W3217002048 on OpenAlexaffvenue
Fatme Hoteit, Debbie Feldman, Lisa C. Carlesso

Bibliographic record

VenuePhysiotherapy Canada · 2021
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonCentre for Interdisciplinary Research in RehabilitationUniversité de Montréal
Fundersnot available
KeywordsOsteoarthritisMedicinePhysical therapyKnee painLogistic regressionChronic painOdds ratioMultinomial logistic regressionInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

Purpose: To explore factors associated with intermittent, constant, and mixed pain in people with knee osteoarthritis. Method: We conducted a secondary analysis of a cross-sectional multicentre study with adults ≥ 40 years with knee osteoarthritis. Participants completed questionnaires on personal (e.g., demographics, comorbidities), physical (e.g., physical function), psychological (e.g., depressive symptoms), pain (e.g., qualities), and tests for physical performance and nervous system sensitivity. We qualified patients’ pain as intermittent, constant, or mixed using the Modified painDETECT Questionnaire and assessed associations with the variables using multinomial logistic regression. Results: The 279 participants had an average age of 63.8 years (SD 9.6), BMI of 31.5 kg/m 2 (SD 8.7), and 58.6% were female. Older age (odds ratio [OR] 0.95; 95% CI: 0.90, 1.00) and higher self-reported physical function (OR 0.94; 95% CI: 0.91, 0.98) were associated with a lower likelihood of mixed pain compared with intermittent pain. Higher pain intensity (OR 1.25; 95% CI: 1.07, 1.47) was related to a 25% higher likelihood of mixed pain compared with intermittent pain. Conclusions: This study provides initial data for associations of personal, pain, and physical function factors with different pain patterns. Awareness of these factors can help clinicians develop targeted strategies for managing patients’ 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.959

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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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