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Record W2108314311 · doi:10.1093/rheumatology/ken339

How do pain and function vary with compartmental distribution and severity of radiographic knee osteoarthritis?

2008· article· en· W2108314311 on OpenAlexaboutno aff
Rachel Duncan, George Peat, Elaine Thomas, Lianne Wood, Elaine M. Hay, Peter Croft

Bibliographic record

VenueLara D. Veeken · 2008
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsMedicineOsteoarthritisRadiographyCompartment (ship)Internal medicineKnee painSeverity of illnessArthropathyPopulationPhysical therapySurgeryPathology

Abstract

fetched live from OpenAlex

OBJECTIVES: In radiographic OA (ROA) of the knee, how does radiographic severity and pattern of compartmental involvement influence symptoms? METHODS: Population-based study of 819 adults aged > or =50 yrs with knee pain. The severity of knee pain and function were measured using the Western Ontario and McMaster Universities scale. Three radiographic views of the knees were obtained. RESULTS: Seven hundred and seventy-seven participants were eligible (mean age 65.5 yrs, 357 males). Higher ROA severity in each of the tibiofemoral (TF) and patellofemoral (PF) compartments was independently associated with higher mean pain scores (TF: F(2, 700) = 9.0, P < 0.0001, PF: F(2, 700) = 12.7, P < 0.0001). The same pattern was found for mean function scores (TF: F(2, 705) = 7.1, P = 0.001, PF: F(2, 705) = 15.9, P < 0.0001). If either the TF or PF compartment was affected by moderate/severe OA, the added presence of OA in the other compartment did not increase the mean pain or function scores. CONCLUSIONS: It is the severity of radiographic disease within a compartment, rather than the distribution of radiographic disease between compartments that is associated with symptoms. ROA in the PF joint is associated with symptoms, emphasizing the importance of radiographic changes in his joint.

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 score0.012
Threshold uncertainty score0.444

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 head0.202
Teacher spread0.193 · 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

Citations95
Published2008
Admission routes1
Has abstractyes

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