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Record W2748321892 · doi:10.1177/1179544117725026

Comparison of the 3 Different Injection Techniques Used in a Randomized Controlled Study Evaluating a Cross-Linked Sodium Hyaluronate Combined With Triamcinolone Hexacetonide (Cingal) for Osteoarthritis of the Knee: A Subgroup Analysis

2017· article· en· W2748321892 on OpenAlexaff
Robert G. McCormack, Martin Lamontagne, Christopher Vannabouathong, Robert T Deakon, Étienne L. Belzile

Bibliographic record

VenueClinical Medicine Insights Arthritis and Musculoskeletal Disorders · 2017
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversité LavalHalTechUniversité de MontréalUniversity of British Columbia
Fundersnot available
KeywordsSodium hyaluronateOsteoarthritisSubgroup analysisTriamcinolone acetonideMedicineSalineRandomized controlled trialAnesthesiaCorticosteroidMeta-analysisSurgeryInternal medicine

Abstract

fetched live from OpenAlex

A recent trial demonstrated that patients with knee osteoarthritis treated with a sodium hyaluronate and corticosteroid combination (Cingal) experienced greater pain reductions compared with those treated with sodium hyaluronate alone (Monovisc) or saline up to 3 weeks postinjection. In this study, injections were administered by 1 of 3 approaches; however, there is currently no consensus on which, if any, of these techniques produce a more favorable outcome. To provide additional insight on this topic, the results of the previous trial were reanalyzed to determine whether (1) the effect of Cingal was significant within each injection technique and (2) pain reductions were similar between injection techniques across all treatment groups. Greater pain reductions with Cingal up to 3 weeks were only significant in the anteromedial subgroup. Across all therapies, both the anteromedial and anterolateral techniques demonstrated significantly greater pain reductions than the lateral midpatellar approach at 18 and 26 weeks.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.439
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.039
GPT teacher head0.399
Teacher spread0.360 · 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".

Quick stats

Citations10
Published2017
Admission routes1
Has abstractyes

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