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Record W2913928846 · doi:10.31138/mjr.29.4.207

Functional Status in Knee Osteoarthritis and its Relation to Demographic and Clinical Features

2018· article· en· W2913928846 on OpenAlexaboutno aff
Faiq I. Gorial, Shams Al-Sabah Anwer Sabah, Mena Baqer Kadhim, Norhan Badri Jamal

Bibliographic record

VenueMediterranean Journal of Rheumatology · 2018
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACOsteoarthritisMedicinePhysical therapyBody mass indexInternal medicineRheumatologyCorrelationSeverity of illnessPathology

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the functional status in a cohort of Iraqi patients with knee Osteoarthritis (OA) and its relation to demographic and clinical features. PATIENTS AND METHODS: This cross-sectional study was conducted on 150 patients with knee OA diagnosed according to the American College of Rheumatology Criteria for classification knee OA. Patients' age, gender, body mass index (BMI), smoking history, educational level, and disease duration were recorded. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score was used to measure functional status of patients with knee OA. RESULTS: . The mean of total WOMAC score was 8.05±2.10 (Range 3-12). The mean WOMAC of: pain score was 3.22 ±0.76 (1-4), stiffness score was 2.05±1.01 and for functional disability score was 2.79±0.88. There was a positive significant correlation between age of the patients and severity of knee OA assessed with total WOMAC score (p=0.026). However, there was a significant negative correlation between educational level and total WOMAC score (p=0.015). CONCLUSIONS: Functional status in knee OA was impaired and there was a statistically positive significant correlation between age of the patients and severity of knee OA with functional impairment. Also, significant negative correlation was demonstrated between educational level and functional impairment.

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 score0.269
Threshold uncertainty score0.447

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 head0.025
GPT teacher head0.290
Teacher spread0.265 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

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