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Record W1716048418 · doi:10.3233/ies-2010-0383

Spectral analysis of knee isokinetic extension curves of osteoarthritic patients

2010· article· en· W1716048418 on OpenAlexaff
Sivan Almosnino, Elizabeth A. Sled, Patrick A. Costigan, Alison Chalmers, Joan M. Stevenson

Bibliographic record

VenueIsokinetics and Exercise Science · 2010
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsMedicineExtension (predicate logic)OrthodonticsComputer science

Abstract

fetched live from OpenAlex

Isokinetic assessment of knee extensors muscle group function in patients suffering from knee osteoarthritis has exclusively been achieved using time-domain based variables. While useful, these analyses do not quantify the conspicuously irregular moment-time pattern exhibited by the involved knee when compared to the contralateral knee. The purpose of this investigation was to assess whether the frequency content of the isokinetic curve can quantify this latter phenomenon. Thirty two patients with knee osteoarthritis participated in this study. Each participant performed 5 maximal concentric knee extensions bilaterally at 60°/s using an isokinetic dynamometer. For each time-domain extension curve, the power spectrum was calculated via Fast Fourier Transform and the maximum frequency content value contained within 99% of total signal power was extracted. Of the 32 participants, 26 exhibited higher frequency contents in the isokinetic curves obtained from their involved knee (p= 0.002, effect size =0.35). The results suggest that the frequency content of isokinetic moment-time curves may provide useful quantitative information regarding the smoothness of moment production, and as such, may be used in conjunction with traditional time-domain variables in the assessment of knee extensors function in patients suffering 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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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