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Record W3111045312 · doi:10.32598/irj.18.2.931.1

The Correlation of Supra Patella Effusion With Pain and Disability in Patients With Knee Osteoarthritis

2020· article· en· W3111045312 on OpenAlexaboutno aff
Anahita Hasannejad, Hasan Namvar, Kamran Ezzati, Fateme Ghiasi, Mohammad Hosseinifar, Asghar Akbari, Amir Salari

Bibliographic record

VenueIranian Rehabilitation Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsnot available
Fundersnot available
KeywordsEffusionMedicineOsteoarthritisPatellaWOMACPhysical therapyKnee painAnterior knee painCorrelationSurgeryPathologyAlternative medicine

Abstract

fetched live from OpenAlex

Objectives: The present research aimed to evaluate the relationship of supra patella effusion with pain and disability in patients with knee osteoarthritis by Ultrasonography (US). Methods: In a cross-sectional study, 60 patients with knee OA (Mean±SD score of body mass index: 29.81±5.64 kg/m2 and age: 50.48±7.57 years) were selected by nonprobability sampling method. Supra patella effusion was evaluated using an US. All study subjects completed the Visual Analogue Scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) for pain and disability outcomes, respectively. To evaluate the relationship between effusion, disability, and pain, the Pearson’s correlation coefficient was employed. Results: There was a poor but significant relationship between the area of effusion (r=0.27, P=0.03), the thickness of effusion (r=0.32, P=0.01), with pain. No correlation was found between the trace of effusion (r=-0.08, P=0.5) and pain. The area of effusion (r=0.1, P=0.17), the thickness of effusion (r=0.08, P=0.51), and the trace of effusion (r=0.0, P=0.9) were not correlated with disability. Discussion: The effusion of supra patella was slightly correlated with pain. In contrast, the effusion of supra patella demonstrated no correlation with disability.

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 score0.176
Threshold uncertainty score0.231

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 head0.004
GPT teacher head0.168
Teacher spread0.165 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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