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Record W4214893280 · doi:10.1097/corr.0000000000002153

Does the Nice Knot Offer Less Elongation Than the Modified Prusik Knot? An In Vitro Study in Cadaver Quadriceps Tendons

2022· article· en· W4214893280 on OpenAlexaff

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2022
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of ManitobaOrthopaedic Innovation CentrePan Am Clinic
FundersAgency for Healthcare Research and Quality
KeywordsCadaverKnot (papermaking)NiceQuadriceps muscleElongationIn vivo

Abstract

fetched live from OpenAlex

BACKGROUND: ACL graft-suture fixation can be constructed with needle or needleless techniques. Needleless techniques have the advantage of decreased injury, preparation time, and cost. The Nice knot is common among upper extremity procedures, and has been shown to have higher load to failure and less elongation compared with other double loop knots; however, there are no studies that have looked at its use for ACL graft-suture construct to determine whether it offers less elongation relative to other needleless techniques. QUESTIONS/PURPOSES: In a cadaver quadriceps tendon model, we asked: (1) Does the Nice knot have less elongation than the Prusik knot? (2) Does the Nice knot have increased peak load and stiffness compared with the Prusik knot? (3) What were the modes of failure of each knot? METHODS: Sixteen quadriceps tendon grafts were harvested from 16 cadaver knee specimens. The median (range) age of the donors was 80 years (70 to 96) and included three male and five female donors. Eight grafts were prepared with the Prusik knot and eight with the Nice knot using a braided polyblend suture. The graft-suture constructs were mounted in a materials testing machine and subjected to a tensile loading protocol beginning with pretensioning of three cycles from 0 to 100 N at 1 Hz followed by a constant load of 50 N for 1 minute then cyclic loading of 200 cycles from 50 to 200 N at 1 Hz. The constructs were loaded to failure as the final step of the loading protocol. Elongations of the construct after each loading step, peak load, stiffness, and graft cross-sectional area were compared. RESULTS: Construct elongations (median [IQR]) for the Nice knot were lower than that of the Prusik knot after pretensioning (4.4 mm [0.8] versus 5.7 mm [1.4]; p = 0.02), preloading (0.6 mm [0.3] versus 1.0 mm [0.3]; p = 0.005), and cyclic loading (7.4 mm [1.4] versus 10.9 mm [2.1]; p = 0.005). Peak load was not different for the Prusik knot construct compared with the Nice knot (334 N [43] versus 312 N [13]; p = 0.08). Stiffness of the Prusik knot construct (103 N/mm [17]) was no different than the Nice knot construct (110 N/mm [13]; p = 0.13). Graft cross-sectional area of the Prusik knot constructs (85 mm2 [35]) were similar to the grafts of the Nice knot constructs (97 mm2 [31]; p = 0.28). Failure mode of the constructs did not differ between groups; it was caused by suture rupture near the knots that secured the free suture ends to the machine and was seen in all 16 tests. CONCLUSIONS: The results of this biomechanical study show that the Nice knot construct has similar or greater biomechanical properties compared with the Prusik knot in the graft suture construct, although the magnitude of the differences are not likely to the level of clinical importance. CLINICAL RELEVANCE: The Nice knot offers an attractive alternative option for needleless ACL graft preparation technique. Future studies should consider comparison to established needle techniques such as Krackow or whipstitch and testing in an intraarticular component in an in vivo model.

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)0.003
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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.170
GPT teacher head0.467
Teacher spread0.296 · 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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Citations6
Published2022
Admission routes1
Has abstractyes

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