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Record W2528411961 · doi:10.5152/eurjrheum.2015.0019

Short-term effect of the combination of hyaluronic acid, chondroitin sulfate, and keratin matrix on early symptomatic knee osteoarthritis

2015· article· en· W2528411961 on OpenAlexaboutno aff
Felice Galluccio, Tatiana Barskova, Marco Matucci‐Cerinic

Bibliographic record

VenueEuropean Journal of Rheumatology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProteoglycans and glycosaminoglycans research
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisMedicineChondroitin sulfateHyaluronic acidWOMACAdverse effectChondroitinNaproxenInternal medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: In the last years, symptomatic slow-acting drugs for osteoarthritis (SYSADOA) have been vastly studied and have generated considerable interest among clinicians. SYSADOA are generally used as a ground therapy with the main rationale to reduce the consumption of nonsteroidal anti-inflammatory drugs (NSAIDs) and thus limit the related adverse events. MATERIAL AND METHODS: In this study, we evaluated the short-term effect of an oral combination of hyaluronic acid, chondroitin sulfate, and keratin matrix on early symptomatic knee osteoarthritis. Forty patients were treated for 1 month and were allowed to assume analgesics or NSAIDs if necessary. RESULTS: At 2 months, the mean reduction of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score was 36% (p<0.001), and the mean reduction of the WOMAC pains score was 40% (p<0.001). Only two patients reported a sporadic need to assume analgesics; no patient reported any side effect during the study period. CONCLUSION: This data demonstrates that the oral combination of hyaluronic acid, chondroitin sulfate, and keratin matrix is safe, well tolerated, and shows a rapid action reducing pain and improving joint function and stiffness in early symptomatic knee osteoarthritis.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.404

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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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 designBench or experimental
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
Published2015
Admission routes1
Has abstractyes

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