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Record W4298124472 · doi:10.1097/bto.0000000000000607

Anatomic Posterolateral Corner Reconstruction With Single Graft Tibial Socket Fixation

2022· article· en· W4298124472 on OpenAlexaff
Brendan Swift, Mohammad M. Alzahrani, Jeffrey Potter, Michael Pickell

Bibliographic record

VenueTechniques in Orthopaedics · 2022
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsVancouver General HospitalUniversity of British ColumbiaOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineNeurovascular bundleFixation (population genetics)TibiaSurgeryHamstringTendonLigament

Abstract

fetched live from OpenAlex

Introduction: The posterolateral corner (PLC) is comprised of the fibular collateral ligament, popliteus tendon, and popliteofibular ligament. Injuries to the PLC are associated with significant morbidity and functional limitation, most frequently manifested through a varus thrust gait. In the previous 2 decades, advances have been made in understanding the importance of the PLC and as a result, many techniques have been developed to address its reconstruction. Material and Methods: The Laprade technique is a previously described anatomic reconstruction of the PLC. We propose some modifications to this technique, which involve dissection of the posterolateral tibia to allow direct protection of the popliteal neurovascular bundle while establishing tibial fixation. A single hamstring graft is utilized for the reconstruction, is routed through the fibular tunnel and subsequently secured with the use of a dual-expanding tenodesis anchor, placed in a tibial socket removing the need for a tibial tunnel. Conclusion: The present study describes a novel anatomic technique that allows for improved protection of neurovascular structures, better control of graft tensioning and tunnel management, and the judicious use of a single tendon autograft while maintaining the described benefits of the anatomic Laprade technique.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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