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Record W7098524218

Restorative Effect of Vitamin D Deficiency on Knee Pain and Quadriceps Muscle Strength in Knee Osteoarthritis

2014· article· en· W7098524218 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisMuscle weaknessvitamin D deficiencyKnee painVitamin D and neurologyKnee JointMuscle strength
DOInot available

Abstract

fetched live from OpenAlex

Abstract- Both vitamin D deficiency and quadriceps muscle weakness are associated with knee osteoarthritis (KOA) and pain. The aim of this study was to determine the restorative effect of vitamin D deficiency on pain and quadriceps muscle strength in knee osteoarthritis. Patients with KOA aged≥30 years, the presence of knee pain for at least one month or longer and serum 25-hydroxyvitamin (25-OHD) deficiencies were recruited in the study. Participants with KOA compatible with Kellgren-Lawrence grade 4, joint instability, and effusion, history of surgery or inflammatory arthropathies were excluded. Serum 25-OHD was assessed by ELISA method and concentrations<20 ng/ml was considered deficiency. Quadriceps muscle strength was measured by dynamometry method and intensity of knee pain by Western Ontario and McMaster University Osteoarthritis index scored by Likert scale and visual analog scale. All participants received 50.000 IU oral cholecalciferol weekly for at least two months. The influence of raising serum 25-OHD on quadriceps muscle strength and pain was assessed by calculation of mean changes from baseline at the end of the treatment period using paired t-test. A total of 67 patients with mean age of 50±6.6 years of age were treated for 2 months. Serum 25-OHD reached to sufficient levels in all except one patient. At the end of the study period, serum 25-OHD and quadriceps muscle strength increased significantly as compared with baseline (P=0.007 and P=0.002, respectively), whereas knee pain

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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 designOther design
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
Published2014
Admission routes1
Has abstractyes

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