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Effect and significance of Vitamin E on oxgen free radicals levels in patients with osteoarthritis

2018· article· en· W3032004028 on OpenAlexaboutno aff
Zhaoming Cheng, Suqin Wang, Luchang Zhuang, Xiaolin Liu, Kang Feng

Bibliographic record

VenueCentral Plains Medical Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisWOMACMalondialdehydeSuperoxide dismutaseMedicineInternal medicineVitamin CDismutaseSodium hyaluronateVitamin ENitric oxideGastroenterologyRadicalEndocrinologyAntioxidantSurgeryOxidative stressBiochemistryChemistryPathology

Abstract

fetched live from OpenAlex

Objective To investigate the clinical effect of Vitamin E on patients with osteoarthritis (OA) and its effect on oxygen free radical levels. Methods Forty-seven patients (48 knees) with OA were randomly divided into study group[24 cases (24 knees)]and control group[23 cases (24 knees)]. Patients in study group were treated with Vitamin E and sodium hyaluronate injection, and patients in control group were treated with sodium hyaluronate injection. The levels of serum nitric oxide(NO), malondialdehyde (MDA) and superoxide dismutase (SOD) before and after treatment in the two groups were detected; and the function of knee joint were obseroed and evaluated with the Western Ontario and McMaster Universities osteoarthritis index (WOMAC) and index of severity of osteoarthritis (ISOA). Results The levels of NO and MDA in study group were significantly lower than those in control group, but the level of SOD in study group was significantly higher than that in control group (P<0.05). Conclusions Vitamin E can effectively reduce the levels of NO and MDA and increase the activity of serum SOD in osteoarthritis patients, scavenge oxygen free radicals and improve joint function. Key words: Vitamin E; Osteoarthritis; Oxygen free radicals; Superoxide dismutase

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.236
Teacher spread0.229 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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