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Record W2130428674 · doi:10.3138/ptc.58.4.293

Acustim in the Treatment of Knee Osteoarthritis: A Single-Subject Research Design

2006· article· en· W2130428674 on OpenAlexvenueno aff
Sinéad P. O'Sullivan

Bibliographic record

VenuePhysiotherapy Canada · 2006
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Venom Research
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisPhysical therapyMedicinePhysical medicine and rehabilitationVisual analogue scaleIntervention (counseling)Multiple baseline designSingle-subject designPsychologyAlternative medicine

Abstract

fetched live from OpenAlex

Purpose: Acustim provides a non-invasive means for controlling pain and restoring function. The following research question was addressed in this study: Does stimulation of specific acupuncture points via acustim reduce impairment and functional limitation among patients with knee osteoarthritis (OA)? Methods: A single-subject research design was employed and replicated across three subjects. Data were collected over two specified time periods, baseline and intervention, for measures of pain, stiffness and function (as assessed by two items on the Patient Specific Functional Scale). During the intervention phase, acustim was administered. Celeration lines and two standard deviation methods were used to analyze the data. In addition, the Lower Extremity Functional Scale (LEFS) was administered at three time points: prebaseline, pre-intervention and post-intervention. Results: During the intervention phase, pain and stiffness were considerably reduced in all subjects (. 228 mm on a visual analog scale). A clinically important difference in function (decrease of .9 points on the LEFS) was demonstrated for all subjects between pre-baseline and post-intervention. Conclusions: Acustim may be an effective treatment for knee OA. Given the ease with which acustim can be administered, this modality may be promising for treating other joints affected by OA.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.383

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 studies0.0000.000
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 head0.108
GPT teacher head0.405
Teacher spread0.297 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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