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Record W7117359127 · doi:10.1055/a-2779-0493

Arthroscopic Lysis of Adhesions for the Management of Arthrofibrosis Following Total Knee Arthroplasty

2025· article· en· W7117359127 on OpenAlexaboutno aff
Ivan Bandovic, Giles R. Scuderi

Bibliographic record

VenueThe Journal of Knee Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsArthrofibrosisPeriprostheticComplicationArthroplastyViscosupplementationTotal knee arthroplastyOsteoarthritisRehabilitation

Abstract

fetched live from OpenAlex

Abstract: Arthrofibrosis remains a challenging complication to manage following total knee arthroplasty (TKA). Early arthrofibrosis, occurring within 12 weeks of TKA, is more responsive to manipulation under anesthesia, whereas late presentations often require surgical intervention. Arthroscopic lysis of adhesions (aLOA) has emerged as a reliable treatment when non-operative measures fail. The procedure involves thorough arthroscopic debridement followed by gentle manipulation and immediate rehabilitation. Published literature has demonstrated that aLOA consistently improves knee ROM by approximately 20 to 60 degrees, with corresponding gains in Knee Society Scores and Western Ontario and McMaster Universities Osteoarthritis (WOMAC) indices, and reductions in pain. Although overall complication rates are rare, large database analyses warn of non-trivial risks, including recurrent stiffness, surgical site infection, and periprosthetic joint infection, with outcomes influenced by factors such as younger age, higher comorbidity burden, poor baseline ROM, and elevated body mass index. Careful patient selection, preoperative exclusion of mechanical or infectious causes of stiffness, and intensive postoperative rehabilitation are critical to the success of this procedure. When applied in appropriately selected patients, aLOA offers meaningful improvement in motion and function and represents a key therapeutic option in the management of arthrofibrosis.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.018
GPT teacher head0.277
Teacher spread0.260 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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