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Record W2071230871 · doi:10.1097/nor.0b013e3181c8ce23

Use of Inpatient Continuous Passive Motion Versus No CPM in Computer-Assisted Total Knee Arthroplasty

2010· article· en· W2071230871 on OpenAlexaboutno aff
Martha Alkire, Michael L. Swank

Bibliographic record

VenueOrthopaedic Nursing · 2010
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsContinuous passive motionTotal knee arthroplastyMedicineSurgeryRange of motion

Abstract

fetched live from OpenAlex

BACKGROUND: Continuous passive motion (CPM) has shown positive effects on tissue healing, edema, hemarthrosis, and joint function (L. Brosseau et al., 2004). CPM has also been shown to increase short-term early flexion and decrease length of stay (LOS) ( L. Brosseau et al., 2004; C. M. Chiarello, C. M. S. Gundersen, & T. O'Halloran, 2004). The benefits of CPM for the population of patients undergoing computer-assisted total knee arthroplasty (TKA) have not been examined. PURPOSE: The primary objective of this study was to determine whether the use of CPM following computer-assisted TKA resulted in differences in range of motion, edema/drainage, functional ability, and pain. METHODS: This was an experimental, prospective, randomized study of patients undergoing unilateral, computer-assisted TKA. The experimental group received CPM thrice daily and physical therapy (PT) twice daily during their hospitalization. The control group received PT twice daily and no CPM during the hospital stay. Both groups received PT after discharge. Measurement included Knee Society scores, Western Ontario McMaster Osteoarthritis Index values, range of motion, knee circumference, and HemoVac drainage. Data were collected at various intervals from preoperatively through 3 months. RESULTS: Although the control group was found to be higher functioning preoperatively, there was no statistically significant difference in flexion, edema or drainage, function, or pain between groups through the 3-month study period.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.259
Teacher spread0.242 · 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.

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

Citations51
Published2010
Admission routes1
Has abstractyes

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