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Record W2781557460 · doi:10.1177/1179544117751622

Rheological Properties of Commercially Available Hyaluronic Acid Products in the United States for the Treatment of Osteoarthritis Knee Pain

2018· article· en· W2781557460 on OpenAlexaff
Mathew Nicholls, Ajay Manjoo, Peter Shaw, Faizan Niazi, Jeffrey Rosen

Bibliographic record

VenueClinical Medicine Insights Arthritis and Musculoskeletal Disorders · 2018
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHyaluronic acidOsteoarthritisMedicineKnee painPhysical therapyBioinformaticsAlternative medicinePhysical medicine and rehabilitationPathologyBiologyAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: The inconsistent results within the current literature regarding the efficacy of intra-articular-hyaluronic acid (IA-HA) for the treatment of knee osteoarthritis (OA) have been suggested to be due to intrinsic differences between individual HA products. The purpose of this investigation is to define the rheological differences between currently available HA products in the United States at the time of this study for the treatment of knee OA, which will help elaborate on the appropriateness of classifying HA products as a class opposed to as individual agents. METHODS: The rheological parameters for Euflexxa, Orthovisc, Supartz, Monovisc, Synvisc, Synvisc-One, Gel-One, and Hyalgan were obtained with a TA AR 2000 EX Rheometer with a cone-plate geometry (40-mm plate diameter and a 2° cone angle) at room temperature. RESULTS: The bulk rheological parameters of the different products suggest molecular structures traversing the range of dilute solution (Hyalgan, Supartz), semidilute solution (Euflexxa, Orthovisc), entangled solutions (Monovisc, Synvisc, Synvisc-One), and even gel-like (Gel-One) behavior. CONCLUSIONS: Due to the differences in rheological properties between IA-HA products, the universal assessment of these products as a class may not be appropriate. Instead, it may be more appropriate to assess each product individually. Future research should aim to link these differences in rheological properties to the differences in clinical efficacy seen across these IA-HA products.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
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 head0.050
GPT teacher head0.309
Teacher spread0.259 · 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.

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

Citations42
Published2018
Admission routes1
Has abstractyes

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