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Record W3044321476 · doi:10.1177/2325967120938039

Biomechanical Comparison of Graft Preparation Techniques for All-Inside Anterior Cruciate Ligament Reconstruction

2020· article· en· W3044321476 on OpenAlexaff
Julia Bowes, Nada Mohamed, Jonelle Jn Baptiste, Lindsey Westover, Catherine Hui, Mark Sommerfeldt

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFixation (population genetics)MedicineAnterior cruciate ligamentStiffnessPost hocBiomechanicsFibrous jointSurgeryBiomedical engineeringOrthodonticsAnatomyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Background: All-inside anterior cruciate ligament reconstruction (ACLR) is an emerging technique used to treat ACL injuries. The all-inside technique uses a 4-stranded graft made from a single tendon that is looped on itself. The 4 strands of the graft must be secured to each other to become a closed-loop structure. Various suture configurations exist to secure the graft to adjustable loop devices, and there is a lack of data to support one technique over another. In addition to the primary sutures used to fasten the graft together, accessory sutures can be tied over the button as secondary fixation. Purpose: To evaluate biomechanical properties of 4-stranded grafts prepared in 5 different configurations. Study Design: Controlled laboratory study. Methods: Porcine flexor tendons (N = 25) were prepared in 5 different configurations (n = 5 tendons per group): simple-interrupted sutures (unsecured fixation), side-to-side fixation with and without secondary fixation, and end-to-end fixation with and without secondary fixation. The grafts were put through the same mechanical testing protocol (cyclic loading, pull to failure) to measure average load at graft failure, average displacement at failure, average stiffness, and average elongation rate. Differences between graft preparation techniques were investigated using 1-way analyses of variance (ANOVAs) with post hoc t tests ( P < .05). Results: Significant 1-way ANOVAs for each biomechanical property were found. Unsecured fixation was the weakest graft preparation with the lowest stiffness (167 ± 12 N/mm), lowest ultimate failure load (637 ± 99 N), and highest elongation rate (0.0033 ± 0.0007 mm/s). End-to-end fixation without secondary fixation showed the highest ultimate failure load (846 ± 26 N), highest stiffness (212 ± 10 N/mm), and lowest rate of elongation (0.0025 ± 0.0001 mm/s). End-to-end fixation, both with and without secondary fixation, as well as side-to-side fixation with secondary fixation showed significantly higher ultimate failure loads than grafts with unsecured fixation. End-to-end fixation performed better than side-to-side fixation; however, for most variables, the difference was not statistically significant. Secondary fixation did not provide significant improvement. Conclusion: The all-inside ACL graft with simple-interrupted sutures is biomechanically inferior to a graft that has its free ends secured to the adjustable tibial loop. Adding secondary fixation to the tibial button does not significantly change the biomechanical properties. Further clinical studies are required to determine whether these findings translate into differences in clinical outcome. Clinical Relevance: All-inside ACLR is gaining popularity in hamstring ACL reconstructive techniques. These results provide surgeons with guidance on the best graft preparation method when using a single quadrupled hamstring tendon graft.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.339
Teacher spread0.312 · 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 designBench or experimental
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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Citations11
Published2020
Admission routes1
Has abstractyes

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