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Record W4414431722 · doi:10.2196/preprints.84011

Assessing Recovery in Total Knee Arthroplasty: A critical narrative review of Muscle Mass, Strength, Physical Performance and Patient Reported Outcome Measures. (Preprint)

2025· article· en· W4414431722 on OpenAlexaboutno aff
Abderrahmane Boukabache, Nimalan Maruthainar, Vikrant Manhas, Darren J. Player

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsNarrative reviewOsteoarthritisPatient-reported outcomeSystematic reviewRange of motionMEDLINEOutcome (game theory)Ceiling effectTotal knee arthroplasty

Abstract

fetched live from OpenAlex

BACKGROUND Total Knee Arthroplasty (TKA) is the primary treatment for advanced knee osteoarthritis. Despite its clinical success and favourable Patient-Reported Outcome Measures (PROMs), approximately 20–30% of patients continue to experience persistent functional limitations and muscle weakness. This highlights the need for a comprehensive evaluation of recovery parameters beyond pain and Range of Motion (ROM). Given the wide range of methods available for assessing TKA outcomes, clinicians often select tools based on personal preference and understanding, which may affect accuracy and consistency; for example, the Knee Injury and Osteoarthritis Outcome Score (KOOS) may overestimate function compared to gait analysis studies. OBJECTIVE The aim of this study was to conduct a narrative review focusing on the utility, strengths and limitations of different outcome measures used in routine orthopaedic practice to optimise post-TKA evaluation. METHODS A literature search was conducted in February 2025 across two databases (PubMed and Web of Science). Eligible studies included original research articles, systematic reviews, and meta-analyses, that focused on validated measures used to evaluate TKA. Case reports, conference abstracts, and studies focused exclusively on surgical techniques were excluded. Themes were identified across studies to structure the results according to types of assessments and clinical applicability. RESULTS A total of 6,831 studies were retrieved and screened in this review, with four themes emerging around muscle mass, strength, performance and PROMs. The Oxford Knee Score (OKS) is favoured for its ease of use and minimal ceiling effects. Broader tools like KOOS and Western Ontario and McMaster Universities Osteoarthritis (WOMAC) provide detailed insights but are less practical clinically. For muscle strength, the Portable Fixed Dynamometer (PFD) showed high reliability and comparability to Isokinetic Dynamometry. Dual-energy X-ray Absorptiometry (DXA) remains the gold standard for assessing muscle mass, while Bioelectrical Impedance Analysis (BIA) offers a practical alternative. The Five-Repetition Sit-to-Stand (5R-STS) test effectively evaluates lower limb power and speed. CONCLUSIONS Clinicians should integrate both objective (muscle mass, strength, performance) and subjective (PROMs) measures to improve TKA recovery assessment. This multidimensional approach not only enhances the accuracy of patient evaluation but also supports the development of tailored rehabilitation strategies that address individual deficits and optimise functional outcomes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.103
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.103
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.320
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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