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Record W2470718031 · doi:10.3233/bmr-160714

Single versus multiple dose hyaluronic acid: Comparison of the results

2016· article· en· W2470718031 on OpenAlexaboutno aff
Demirhan Dıraçoğlu, Tuğba Baysak Tunçay, Tuğba Şahbaz, Cihan Aksoy

Bibliographic record

VenueJournal of Back and Musculoskeletal Rehabilitation · 2016
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsHyaluronic acidChemistryPharmacologyMedicineAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to compare the effectiveness of three injections of standard linear HA versus single injection of lightly cross-linking HA in patients with knee OA. METHODS: Forty subjects were randomized into two groups. The first group received single dose intraarticular injection of 4 ml lightly cross-linking sodium hyaluronate (Monovisc), and the second group received three consecutive intraarticular injections of 2.5 ml standard linear sodium hyaluronate (Adant) with one week intervals. Visual analog scale (VAS)-pain and Western Ontario and McMaster University Osteoarthritis Index (WOMAC) scores were measured. RESULTS: In both groups, VAS-pain and WOMAC scores (except WOMAC-stiffness) were improved statistically lasting up to the 6th month with respect to before injection values (p< 0.001). There were no statistical differences in VAS-pain and WOMAC scores after injections (p> 0.05) in both groups. But in the 6th month visit, VAS-resting values were found to be statistically improved in standard linear HA group compared to lightly cross-linking HA group (p< 0.05). CONCLUSION: Although three-dose administration was significantly superior to single-dose at the sixth month, current knowledge is not sufficient to decide whether single-dose or multiple-dose HA injection should be chosen. There is a clear need for verification of our results with long-term studies on larger patient groups.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.195

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 head0.021
GPT teacher head0.297
Teacher spread0.277 · 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

Citations21
Published2016
Admission routes1
Has abstractyes

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