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Record W2114200395 · doi:10.1155/2015/327161

Depression and the Overall Burden of Painful Joints: An Examination among Individuals Undergoing Hip and Knee Replacement for Osteoarthritis

2015· article· en· W2114200395 on OpenAlexaff
Rajiv Gandhi, Michael G. Zywiel, Nizar N. Mahomed, Anthony V. Perruccio

Bibliographic record

VenueArthritis · 2015
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineOsteoarthritisWOMACDepression (economics)Joint replacementOdds ratioBody mass indexKnee replacementKnee JointPhysical therapyHip replacementOddsArthroplastySurgeryInternal medicineLogistic regressionPathology

Abstract

fetched live from OpenAlex

The majority of patients with hip or knee osteoarthritis (OA) report one or more symptomatic joints apart from the one targeted for surgical care. Therefore, the purpose of the present study was to investigate the association between the burden of multiple symptomatic joints and self-reported depression in patients awaiting joint replacement for OA. Four hundred and seventy-five patients at a single centre were evaluated. Patients self-reported joints that were painful and/or symptomatic most days of the previous month on a homunculus, with nearly one-third of the sample reporting 6 or more painful joints. The prevalence of depression was 12.2% (58/475). When adjusted for age, sex, education level, hip or knee OA, body mass index, chronic condition count, and joint-specific WOMAC scores, each additional symptomatic joint was associated with a 19% increased odds (odds ratio: 1.19 (95% CI: 1.08, 1.31, P < 0.01)) of self-reported depression. Individuals reporting 6 or more painful joints had 2.5-fold or greater odds of depression when compared to those patients whose symptoms were limited to the surgical joint. A focus on the surgical joint alone is likely to miss a potentially important determinant of postsurgical patient-reported outcomes in patients undergoing hip or knee replacement.

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)0.001
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 score0.527
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.017
GPT teacher head0.256
Teacher spread0.238 · 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

Citations43
Published2015
Admission routes1
Has abstractyes

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