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Record W4399368652 · doi:10.1016/j.tvjl.2024.106160

Assessment of a continuous passive motion assistive device in dogs following stifle surgery

2024· article· en· W4399368652 on OpenAlexaff
Andrés Erazo, Kalinne Rocha Pinto, Youngjin Choi, Seok‐Bum Ko

Bibliographic record

VenueThe Veterinary Journal · 2024
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRehabilitationContinuous passive motionMedicineRange of motionPhysical medicine and rehabilitationPhysical therapyBody position

Abstract

fetched live from OpenAlex

Canine rehabilitation optimizes recovery and the quality of life in dogs with musculoskeletal conditions or after surgery. Achieving proper range of motion (ROM) is vital post-stifle surgery, often accomplished through manual therapy and active exercises. We investigated the mechanical performance of a continuous passive motion (CPM) device for dogs and its potential use in canine rehabilitation therapy. In the ethical review process, our research was accepted to be evaluated in a sample of four dogs that had undergone left stifle surgery. Each dog underwent four sessions with the device at three different speeds. Results showed the device replicated extension angles close to goniometer measurements used in manual therapy. Flexion was also achieved, but not to the same extent. A force threshold stopped the device, avoiding discomfort in dogs with restricted ROM. Dog-specific factors like body position, opposition to movement, limb size, stage of recovery, haircoat, and discomfort, appeared to influence device operation. Mechanical improvements to allow for enhanced flexion are recommended in future CPM device designs, including a resistance threshold that could be adjusted for individual dogs and stages of healing. This study serves as a foundation for future advancements in canine rehabilitation systems. A canine CPM device may provide an affordable option to improve ROM. This could be beneficial for dog owners, who may not be comfortable with manual therapy, to assist with home rehabilitation exercises.

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)0.002
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 score0.773
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.080
GPT teacher head0.371
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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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