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Record W2489701077 · doi:10.1016/j.sjpain.2016.07.002

Osteoarthritis patients with pain improvement are highly likely to also have improved quality of life and functioning. A post hoc analysis of a clinical trial

2016· article· en· W2489701077 on OpenAlexaboutno aff
Paul M. Peloso, R Andrew Moore, Wen‐Jer Chen, Hsiao-Yi Lin, Davis Gates, Walter L. Straus, Zoran Popmihajlov

Bibliographic record

VenueScandinavian Journal of Pain · 2016
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePost-hoc analysisQuality of life (healthcare)OsteoarthritisPhysical therapyClinical trialPhysical medicine and rehabilitationAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: This analysis evaluated whether osteoarthritis patients achieving the greatest pain control and lowest pain states also have the greatest improvement in functioning and quality of life. METHODS: Patients (n=419) who failed prior therapies and who were switched to etoricoxib 60mg were categorized as pain responders or non-responders at 4 weeks based on responder definitions established by the Initiative on Methods, Measurement, and Pain (IMMPACT) criteria, including changes from baseline of ≥15%, ≥30%, ≥50%, ≥70% and a final pain status of ≤3/10 (no worse than mild pain). Pain was assessed at baseline and 4 weeks using 4 questions from the Brief Pain Inventory (BPI) (worst pain, least pain, average pain, and pain right now), and also using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain subscale. We examined the relationship between pain responses with changes from baseline in two functional measures (the BPI Pain Interference questions and the WOMAC Function Subscale) as well as changes from baseline in quality of life (assessed on the SF-36 Physical and Mental Component Summaries). We also sought to understand whether these relationships were influenced by the choice of the pain instrument used to assess response. We contrast the mean difference in improvements in the functional and quality of life instruments based on pain responder status (responder versus non-responder) and the associated 95% confidence limits around this difference. RESULTS: Patients with better pain responses were much more likely to have improved functional responses and improved quality of life, with higher mean changes in these outcomes versus pain non-responders, regardless of the choice of IMMPACT pain response definition (e.g., using any of 15%, 30%, 50%, 70% change from baseline) or the final pain state of ≤3/10. There was an evident gradient, where higher levels of pain response were associated with greater mean improvements in function and quality of life. The finding that greater pain responses led to greater functional improvements and quality of life gains was not dependent on the manner in which pain was evaluated. Five different pain instruments (e.g., the 4 questions on pain from the BPI pain questionnaire and the WOMAC pain subscale) consistently demonstrated that pain responders had statistically significantly greater improvements in function and quality of life compared to pain non-responders. This suggests these results are likely to be generalizable to any validated pain measure for osteoarthritis. CONCLUSIONS: Pain is an efficient outcome measure for predicting broader patient response in osteoarthritis. Patients who do not achieve timely, acceptable pain states over 4 weeks were less likely to experience functional or quality of life improvements. IMPLICATIONS: Good pain improvements in osteoarthritis with a valid pain instrument are a proxy for good improvements in both function and quality of life. Therefore proper osteoarthritis pain assessment can lead to efficient evaluations in the clinic.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.031
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.026
GPT teacher head0.304
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2016
Admission routes1
Has abstractyes

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