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Record W4321250382 · doi:10.3390/diagnostics13040760

Prognostic Factors Related to Clinical Response in 210 Knees Treated by Platelet-Rich Plasma for Osteoarthritis

2023· article· en· W4321250382 on OpenAlexaboutno aff
Clément Chopin, M. Geoffroy, Lukshe Kanagaratnam, Claire Dorilleau, Fiona Ecarnot, Renaud Siboni, Jean‐Hugues Salmon

Bibliographic record

VenueDiagnostics · 2023
Typearticle
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisMedicineWOMACPlatelet-rich plasmaVisual analogue scaleAdverse effectInternal medicineObservational studyPhysical therapySurgeryPlateletPathology

Abstract

fetched live from OpenAlex

Many studies have shown the effectiveness of platelet-rich plasma (PRP) in the treatment of knee osteoarthritis. We aimed to determine the factors associated with good or poor response to PRP injections in knee osteoarthritis. This was a prospective observational study. Patients with knee osteoarthritis were recruited from a university hospital. PRP was injected twice at a one-month interval. Pain was assessed on a visual analog scale (VAS) and function was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Radiographic stage was collected and defined according to the Kellgren-Lawrence classification. Patients were classified as responders if they met the OMERACT-OARSI criteria at 7 months. We included 210 knees. At 7 months, 43.8% were classified as responders. Total WOMAC and VAS were significantly improved between M0 and M7. Physical therapy and a heel-buttock distance >35 cm were the two criteria associated with poor response at M7 by multivariate analysis. Pain VAS at M7 appeared to be lower in patients with osteoarthritis for less than 24 months. No adverse effects were reported. PRP treatment in knee osteoarthritis appears to be well-tolerated and effective, even in patients who reacted poorly to hyaluronic acid. Response was not associated with radiographic stage.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.352
Teacher spread0.315 · 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 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

Citations12
Published2023
Admission routes1
Has abstractyes

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