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Record W2056456537 · doi:10.4103/0974-7753.122964

Evaluation of the degree of knee joint osteoarthritis in patients with early gray hair

2013· article· en· W2056456537 on OpenAlexaboutno aff
Alireza Ashraf, B Kazemi, Mohammad Reza Namazi, Fariba Zarei, Shima Foruzi

Bibliographic record

VenueInternational Journal of Trichology · 2013
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisMedicineWOMACPhysical therapyGrading scaleKnee JointKnee painArthritisInternal medicineSurgeryPathologyAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Osteoarthritis (OA) is the most common form of arthritis and one of the causes of pain and disability. The hair graying characteristic correlates strictly with chronological aging and take places to varying degrees in all individuals, disregarding gender or race. AIMS: Comparison of the degrees of clinical and radiologic severity of the knee OA in individuals with early hair graying compared to ordinary individuals. MATERIALS AND METHODS: A total of 60 patients with knee OA and similar demographic characteristics were enrolled in this study. All patients were classified in to 3 age subgroups in each of the case and control groups (30-40 year, 41-50 year, 51-60 year). In the case group, the patients must had early hair graying, too. Knee OA were classified using the Kellgren-Lawrence (KL) grading scale. Western Ontario McMaster University Osteoarthritis index (WOMAC) was applied to assess clinical severity of the knee OA. RESULTS: The mean ± SD of WOMAC index in the case group was 60.7 ± 15.9 and in the control group was 55.3 ± 15.3 (P = 0.1). The mean rank of KL scale in case group was 35.3 and in the control group was 25.6 (P = 0.02). CONCLUSION: Even at the same age of OA onset, the rate of progression of radiological findings and the grade of joint destruction in individuals with early hair graying are greater than normal individuals. However, clinical and functional relevant remain unclear.

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 score0.317
Threshold uncertainty score0.205

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 head0.032
GPT teacher head0.270
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

Citations0
Published2013
Admission routes1
Has abstractyes

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