MétaCan
Menu
Back to cohort
Record W3124247133 · doi:10.1016/j.eats.2020.09.004

Arthroscopic Medial Meniscal Posterior Root Repair With Transtibial Luggage‐Tag and Horizontal Mattress Sutures

2021· article· en· W3124247133 on OpenAlexaff
David Drynan, Marcel Betsch, Waael Aljilani, Daniel B. Whelan

Bibliographic record

VenueArthroscopy Techniques · 2021
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsSt. Michael's HospitalWomen's College HospitalUniversity of TorontoToronto Western Hospital
Fundersnot available
KeywordsMedicineMedial meniscusFibrous jointMeniscusSurgeryKnee flexionAnatomyOrthodonticsOsteoarthritis

Abstract

fetched live from OpenAlex

Medial meniscal posterior root repair techniques have shown positive yet varied results in the literature. The decision to perform repair has improved clinical outcomes in many situations, although the healing rate is approximately 64% and the repair strength is roughly one-third of the native root strength, with meniscal extrusion being common. We present a technique based on biomechanical evidence to obtain a strong anatomic posterior root repair to restore nearly normal knee mechanics, combining an increased size of footprint under the lateral aspect of the medial meniscal horn for healing and a luggage-tag suture with a posteriorly placed horizontal mattress suture. The horizontal mattress suture is passed to capture the circumferential fibers of the meniscus and the luggage-tag suture is passed to capture the radial fibers of the meniscal body, through a single transtibial tunnel. The aim of this repair is to restore the normal meniscal function.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.001
Research integrity0.0010.001
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 head0.005
GPT teacher head0.258
Teacher spread0.253 · 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 designCase report
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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueArthroscopy TechniquesSame topicKnee injuries and reconstruction techniquesFrench-language works237,207