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Record W4415438943 · doi:10.1302/1358-992x.2025.10.148

NORMATIVE DATA AND THE EFFECT OF GRAFT CHOICE ON ISOKINETIC KNEE MUSCLE STRENGTH IN PAEDIATRIC PATIENTS FOLLOWING ANTERIOR CRUCIATE LIGAMENT RECONSTRUCTION: A RETROSPECTIVE STUDY

2025· article· en· W4415438943 on OpenAlexaff
G. De Petrillo, J-F. Girouard, Thierry Pauyo, Marie‐Lyne Nault, Mark Burman, P-A Martineau, Louis‐Nicolas Veilleux

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsAnterior cruciate ligamentHamstringRetrospective cohort studyAnterior cruciate ligament reconstructionReturn to sportBalance (ability)Muscle strength

Abstract

fetched live from OpenAlex

Return to sport (RTS) criteria after an anterior cruciate ligament reconstruction (ACLR) is typically based on functional tests results and knee strength recovery. Patients aim to reach a limb symmetry index (LSI) of 90% and hamstrings-to-quadriceps (H/Q) ratios of 0.5–0.8. While attaining these postoperative objectives, the risk of re-injury has risen to 30%. Therefore, comparing peak isokinetic torque data to normative data in addition to the aforementioned metrics may add to the current assessment of postoperative strength deficits and readiness to RTS. Strength deficits are typically directly related to the location of the graft harvested. Due to the paucity of studies that compare postoperative outcomes in pediatric patients who either received a hamstring tendon autograft (HTA), quadriceps tendon autograft (QTA), or a patellar tendon autograft (PTA), graft choice remains dependent on the operating surgeon's preference and patients individual needs. The primary objective of this study was to generate normative data from the non-injured knee flexors and extensors isokinetic strength parameters of 13- to 18-year-old patients. The secondary objective was to determine the effects of graft choice on postoperative knee strength and patient-reported outcomes. A retrospective chart review was conducted including patients aged 13–18 years that underwent primary ACLR and performed a standardized bilateral isokinetic knee strength assessment between June 1st 2017 and March 1st 2022 at our institution. Anthropometric data, ACL-RSI and Pedi-IKDC scores, and isokinetic knee strength testing results at 6 to 7 months postoperatively were collected. Normative data calculated using patient's non-operated knee average peak torque relative to body mass at 60 deg/sec, 180 deg/sec, and 300 deg/sec in flexion and extension. Using the norms measured, Z-scores were calculated and compared between grafts. ANOVAs were performed to compare postoperative strength results and patient-reported outcomes between grafts. A total of 286 patients were included in this study. The mean age of patients in the cohort was 15.78& 1.26 years and 63% of the cohort was female. Males and patients with higher BMI had greater average relative peak torque at all three speeds in flexion and extension (p<0.05). HTAs were associated to significantly greater knee extension strength at 60 deg/sec on the operated side (p=0.0001 vs QTA, p=0.007 vs PTA). Patients having received a HTA normalized their average relative peak torque to a greater extent than patients having received a QTA (–0.51 vs −0.78, p=0.0002) or PTA (–0.51 vs −0.97, p=0.008) in flexion at 60 deg/sec. The HTA (n=167) were associated to significantly higher LSI in extension at all three speeds (p<0.05) than QTA (n=45) and PTA (n=55). QTA were associated to a significantly higher LSI in flexion at 60 deg/sec than HTA (p=0.01). ACL-RSI and Pedi-IKDC scores were not significantly different between the three autograft groups (p≥0.05). The postoperative pediatric ACLR knee strength norms calculated can be used as clinical reference values for patients awaiting RTS clearance. Additionally, our findings suggest that HTAs lead to greater and more normal postoperative knee average peak torque relative to body mass and LSI compared to QTAs and PTAs.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.261
Teacher spread0.256 · 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 routes1
Has abstractyes

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