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Record W4281900950 · doi:10.1002/ejp.1988

The effect of duloxetine on mechanistic pain profiles, cognitive factors and clinical pain in patients with painful knee osteoarthritis—A randomized, <scp>double‐blind</scp>, <scp>placebo‐controlled</scp>, crossover study

2022· article· en· W4281900950 on OpenAlexaboutno aff
Kristian Kjær Petersen, Asbjørn Mohr Drewes, Anne Estrup Olesen, Nadia Ammitzbøll, Davide Bertoli, Christina Brock, Lars Arendt‐Nielsen

Bibliographic record

VenueEuropean Journal of Pain · 2022
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersNovo Nordisk FondenShionogiNovo NordiskAalborg UniversitetDanmarks GrundforskningsfondDaiichi Sankyo EuropeNational Research FoundationGigtforeningen
KeywordsDuloxetineOsteoarthritisCrossover studyPlaceboMedicineKnee painAnesthesiaDuloxetine HydrochlorideCognitionPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Duloxetine is indicated in the management of pain in osteoarthritis. Evidence suggests that duloxetine modulates central pain mechanisms and cognitive factors, and these factors are assumed contributing to the analgesic effect. This proof-of-mechanism, randomized, placebo-controlled, crossover, double-blinded trial evaluated the effect of duloxetine on quantitative sensory testing (QST), cognitive factors and clinical pain in patients with osteoarthritis and to predict the analgesic effect. METHODS: Twenty-five patients completed this cross-over study with either 18-week duloxetine (maximum 60 mg/daily) followed by placebo or vice-versa. Pressure pain thresholds, temporal summation of pain and conditioned pain modulation were assessed using cuff algometry. The Hospital Anxiety and Depression Scale and the Pain Catastrophizing Scale evaluated cognitive factors. Clinical pain was assessed using Brief Pain Inventory and Western Ontario and McMaster Universities Osteoarthritis Index. Linear regression models were used to predict the analgesic effect of duloxetine. RESULTS: Depending on the clinical pain outcome, 40%-68% of patients were classified as responders to duloxetine. Linear regression models predicted the analgesic effect (predictive value of 45%-75% depending on clinical pain outcome parameter) using a combination of pretreatment QST parameters, cognitive factors and clinical pain. No significant changes were found for QST, cognitive factors or clinical pain on a group level when comparing duloxetine to placebo. CONCLUSION: A combination of pretreatment QST, cognitive factors and clinical pain was able to predict the analgesic response of duloxetine. However, in this relatively small study, duloxetine did not selectively modulate QST, cognitive factors or clinical pain intensity when compared with placebo. SIGNIFICANCE: Duloxetine is proposed as a treatment for chronic pain. Pre-clinical trials suggest that duloxetine provides analgesia through modulation of descending pain inhibitory pathways or through improvements in cognitive factors. The current study demonstrates that pretreatment mechanistic pain profiling, cognitive factors and clinical pain can predict the analgesic effect of duloxetine and that only a subset of patients might benefit from duloxetine treatment.

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)0.067
metaresearch head score (Gemma)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0670.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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; both teacher heads agree on what is shown here.

Study designRandomized trial
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

Citations24
Published2022
Admission routes1
Has abstractyes

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