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Record W2740937536 · doi:10.1177/2325967117s00249

Anatomic Anterior Cruciate Ligament Reconstruction - A Prospective Evaluation Using Three-Dimensional Magnetic Resonance Imaging

2017· article· en· W2740937536 on OpenAlexaff
Adam Hart, Thiru Sivakumaran, Mark Burman, T. Powell, Paul A. Martineau

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineMagnetic resonance imagingAnterior cruciate ligamentAnterior cruciate ligament reconstructionOrthopedic surgeryFootprintHamstringSurgeryRadiologyOrthodontics

Abstract

fetched live from OpenAlex

Objectives: The recent emphasis on anatomic reconstruction of the anterior cruciate ligament (ACL) is well supported by clinical and biomechanical research. Unfortunately, the location of the native femoral footprint can be difficult or even impossible to see at the time of surgery. Most surgeons therefore rely on anatomic landmarks, custom drill guides, or general rules-of-thumb to guide femoral tunnel placement; however, the accuracy of these techniques to reconstruct each patient’s native anatomy is poorly understood. The objective of this study was to use a previously described isotropic magnetic resonance sequence (3D MRI) to image patients with torn ACLs before and after reconstruction and thereby assess the accuracy of graft position on the femoral condyle in comparison to each patient’s native ACL footprint. Methods: Forty-one patients with unilateral ACL tears were prospectively recruited into our study. Each patient underwent a 3D MRI of both the injured and uninjured knees before surgery. The contralateral (uninjured) knee scan was used to define the patient’s native footprint. Patients then underwent ACL reconstruction with hamstring autograft by one of four experienced fellowship-trained sports orthopedic surgeons. The injured knee was reimaged after surgery. The location and percent overlap of the reconstructed femoral footprint was compared to the patient’s native footprint. Results: The center of the native ACL femoral footprint was a mean of 16.4 +/- 4.6 mm distal and 5.3 +/- 2.9 mm anterior to the apex of the deep cartilage. The position of the reconstructed graft was significantly different, with mean distance of 10.4 +/- 2.7 mm distal (P < 0.0001) and 7.7 +/- 3.1 mm anterior (P = 0.001). The mean distance between the center of the graft and the center of the native ACL femoral footprint (error distance) was 5.7 +/- 3.6 mm. Comparing error distances amongst the four surgeons demonstrated no significant difference using the Kruskal-Wallis one-way ANOVA (P = 0.78). On average, 21% of the graft was within the native ACL femoral footprint. Of the 41 patients, 16 (39%) had the graft placed entirely outside the native ACL footprint. Conclusion: Despite contemporary techniques and a concerted effort to perform anatomic ACL reconstructions by four experienced sports orthopedic surgeons, the position of the femoral footprint was significantly different between the native and reconstructed ligaments. Furthermore, each of the four surgeons uses a different technique but all had comparable errors in their tunnel placements. In order to achieve a truly anatomic reconstruction, surgeons may consider using a pre-operative 3D MRI, which enables excellent visualization of the ACL’s native anatomy and could potentially be used as a roadmap to guide anatomic tunnel placement.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.306
Teacher spread0.290 · 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 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
Published2017
Admission routes1
Has abstractyes

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