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Record W2321633318 · doi:10.1177/0954411913517880

Biochemical analyses of human osteoarthritic and periprosthetic synovial fluid

2014· article· en· W2321633318 on OpenAlexaff
Leah E Guenther, Bryan Wilkinson Pyle, Thomas R. Turgeon, Éric Bohm, U.P. Wyss, Tannin A. Schmidt, Jan-M. Brandt

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of CalgaryUniversity of ManitobaConcordia Hospital
Fundersnot available
KeywordsPeriprostheticSynovial fluidHyaluronic acidLubricantOsteoarthritisMedicineBiomedical engineeringArthroplastyPathologySurgeryMaterials scienceAnatomyComposite material

Abstract

fetched live from OpenAlex

Biochemical analyses were performed on osteoarthritic and periprosthetic synovial fluid in order to propose changes to lubricant specifications currently outlined in orthopaedic wear testing standards. Osteoarthritic and periprosthetic synovial fluid samples were obtained from the hip and knee joints of 40 patients. The samples in each group were analysed and compared in order to identify differences between the protein concentration, constituent fractions, osmolality, thermal stability and the hyaluronic acid concentration and molecular weight distribution of osteoarthritic and periprosthetic synovial fluid. The average total protein concentration was approximately 30 g/L, which was much higher than the 20 g/L currently specified in the knee wear testing standard; however, the 30 g/L protein concentration matched the recently revised standard for hip simulator wear testing. No significant difference was found between the protein concentration, osmolality, thermal stability, and hyaluronic acid concentration of osteoarthritic and periprosthetic synovial fluid. The clinical data provided should be used to better define the composition of a more clinically relevant lubricant for orthopaedic wear testing.

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)0.002
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 score0.066
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.020
GPT teacher head0.274
Teacher spread0.254 · 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

Citations34
Published2014
Admission routes1
Has abstractyes

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Same venueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in MedicineSame topicOrthopaedic implants and arthroplastyFrench-language works237,207