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Record W4395084242 · doi:10.1016/j.arrct.2024.100340

Cryoneurolysis for the Treatment of Knee Arthritis to Facilitate Inpatient Rehabilitation: A Case Report

2024· article· en· W4395084242 on OpenAlexaff
Fraser MacRae, Mahdis Hashemi, Ève Boissonnault, Romain David, Paul Winston

Bibliographic record

VenueArchives of Rehabilitation Research and Clinical Translation · 2024
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of British ColumbiaUniversité de MontréalIsland HealthWestern University
FundersIpsenPacira BioSciences
KeywordsOsteoarthritisMedicineRehabilitationKnee painPhysical therapyAnterior knee painPhysical medicine and rehabilitationSurgeryPatellaAlternative medicine

Abstract

fetched live from OpenAlex

A 65-year-old woman presenting with a sensory ganglionopathy complicated with COVID-19 is limited in her rehabilitation due to pain from lateral compartment knee osteoarthritis. To increase participation in rehabilitation, cryoneurolysis of the medial and lateral anterior femoral cutaneous nerve and infrapatellar branches of the saphenous nerve was provided to manage pain associated with knee osteoarthritis. The patient reported immediate relief from pain. Physiotherapy noted improvement immediately after the procedure. Follow-ups at 7- and 11-days post-treatment revealed ongoing increases in mobility and reduction in pain. The patient was discharged to live independently shortly after cryoneurolysis. Cryoneurolysis for knee osteoarthritis could be considered as a treatment option to increase participation in rehabilitation for hospital inpatients who are stalled in their rehabilitation due to pain and poor mobility from knee osteoarthritis.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.178
GPT teacher head0.468
Teacher spread0.291 · 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 designOther design
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

Citations2
Published2024
Admission routes1
Has abstractyes

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