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Record W4309413272 · doi:10.1123/mc.2021-0124

Heating the Skin Over the Knee Improves Kinesthesia During Knee Extension

2022· article· en· W4309413272 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMotor Control · 2022
Typearticle
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPhysical medicine and rehabilitationExtension (predicate logic)Knee flexionMedicineKnee JointComputer scienceArtificial intelligenceSurgery

Abstract

fetched live from OpenAlex

To determine how heating affects dynamic joint position sense at the knee, participants (n = 11; F = 6) were seated in a HUMAC NORM dynamometer. The leg was passively moved through extension and flexion, and participants indicated when the 90° reference position was perceived, both at baseline (28.74 ± 2.43 °C) and heated (38.05 ± 0.16 °C) skin temperatures. Day 2 of testing reduced knee skin feedback with lidocaine. Directional error (actual leg angle-target angle) and absolute error (AE) were calculated. Heating reduced extension AE (baseline AE = 5.46 ± 2.39°, heat AE = 4.10 ± 1.97°), but not flexion. Lidocaine did not significantly affect flexion AE or extension AE. Overall, increased anterior knee-skin temperature improves dynamic joint position sense during passive knee extension, where baseline matching is poorer. Limited application of lidocaine to the anterior thigh, reducing some skin input, did not influence dynamic joint position sense, suggesting cutaneous receptors may play only a secondary role to spindle information during kinesthetic tasks. Importantly, cutaneous input from adjacent thigh regions cannot be ruled out as a contributor.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.928
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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