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Record W2626251570 · doi:10.1177/1179544117712993

Cost-Utility of a Single-Injection Combined Corticosteroid-Hyaluronic Acid Formulation vs a 2-Injection Regimen of Sequential Corticosteroid and Hyaluronic Acid Injections

2017· article· en· W2626251570 on OpenAlexaff
Étienne L. Belzile, Robert T Deakon, Christopher Vannabouathong, Mohit Bhandari, Martin Lamontagne, Robert G. McCormack

Bibliographic record

VenueClinical Medicine Insights Arthritis and Musculoskeletal Disorders · 2017
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsUniversity of British ColumbiaUniversité de MontréalMcMaster UniversityHalTechUniversité Laval
Fundersnot available
KeywordsCorticosteroidHyaluronic acidRegimenMedicineOsteoarthritisAnesthesiaSurgeryPathologyAlternative medicine

Abstract

fetched live from OpenAlex

Research has shown early and sustained relief with a combination therapy of a corticosteroid (CS) and hyaluronic acid (HA) in knee osteoarthritis (OA) patients. This can be administered via a single injection containing both products or as separate injections. The former may be more expensive when considering only product cost, but the latter incurs the additional costs and time of a second procedure. The purpose of this study was to compare the cost-utility of the single injection with the 2-injection regimen. The results of this analysis revealed that the single-injection formulation of a CS and HA may be cost-effective, assuming a willingness-to-pay of $50 000 per quality-adjusted life year gained, for symptomatic relief of OA symptoms. This treatment may also be more desirable to patients who find injections to be inconvenient or unpleasant.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.044
GPT teacher head0.338
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2017
Admission routes1
Has abstractyes

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