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Record W4415570876 · doi:10.1302/1358-992x.2025.11.041

THE EFFECTS OF PREOPERATIVE BLOOD FLOW RESTRICTION TRAINING IN PATIENTS UNDERGOING ANTERIOR CRUCIATE LIGAMENT RECONSTRUCTION

2025· article· en· W4415570876 on OpenAlexaffabout
Lawrence Wengle, Marcel Betsch, Katrina Dekirmendjian, F. Migliorini, Jas Chahal, Tim Dwyer, John Theodoropoulos

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBlood flow restrictionAnterior cruciate ligament reconstructionHamstringAnterior cruciate ligamentRehabilitationTourniquetRandomized controlled trialClinical trial

Abstract

fetched live from OpenAlex

Arthrogenic muscle inhibition (AMI) involving the quadricep femoris (QF) muscle group is a common finding in patients undergoing anterior cruciate ligament (ACL) reconstruction, particularly in Canada where wait times for elective surgery are lengthy. In order to combat the challenges of AMI for patients with an ACL injury, blood flow restriction (BFR) training has become an effective new training modality for the rehabilitation of these patients. This has been shown to have beneficial effects in the post-operative period by reducing QF atrophy. However, the use of BFR in the pre-operative setting has yet to be well established. The specific aims of this study were to determine if preconditioning with BFR training in patients awaiting ACL surgery (1) improves QF muscle strength testing and (2) reduces QF muscle group atrophy. We hypothesized that preconditioning with BFR in patients awaiting ACL reconstruction has a beneficial effect on clinical outcomes prior to surgery. All patients over the age of 18 who had a clinical and radiological diagnosis of isolated complete ACL injury and were awaiting surgical intervention were considered eligible for the study. Eligible patients were randomized into the BFR or CONTROL groups. All patients attended 12 physiotherapy sessions involving closed kinetic chain exercises on the affected leg using a leg press machine. The BFR tourniquet was inflated throughout the protocol for the BFR group and left deflated for the CONTROL group. Objective quadriceps and hamstring strength assessments using a Biodex machine were performed at the beginning of the study and at the completion of physiotherapy. Both the affected leg (ACL injury) and unaffected leg (control leg) were evaluated. This study compared pre-intervention vs. post-intervention differences between the two groups. Our primary outcome measure was peak torque (ft-lbs) measured for both knee flexion and extension. Our secondary outcome measures included thigh circumference (mm) and patient reported outcome measures (KOOS and SF-12). To date, 30 of 32 patients have been enrolled in the study and 21 of 32 patients have completed the protocol (BFR = 14, CONTROL = 7). Biodex testing has demonstrated an average increase in affected knee extension strength of 15% and 27% for the BFR and CONTROL groups respectively. Additionally, unaffected knee extension strength increased by 9% in the BFR group and 12% in the CONTROL group. Thigh circumference of the affected knee increased by an average of 9 mm in the BFR group and 15 mm in the CONTROL group. No reported complications or negative effects involving BFR have been reported. Preliminary results have demonstrated an overall strength increase in both BFR and CONTROL groups. The affected knee in both treatment groups have exhibited a greater increase in strength compared to the unaffected knee. No significant statistical differences between the two treatment groups have been noted thus far. This may be attributable to a small sample size and lack of CONTROL patients that have completed the study. Further statistical analysis and comparison between groups is pending upon completion of all 32 patients. If BFR in the pre-operative setting can be shown to provide beneficial effects, it will be a valuable tool in the treatment of AMI among patients with ACL injury.

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)0.001
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 score0.712
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.004
GPT teacher head0.219
Teacher spread0.215 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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