MétaCan
Menu
Back to cohort
Record W3122420200 · doi:10.1016/j.eats.2020.10.045

Capsular Management of the Hip During Arthroscopic Acetabular Chondral Resurfacing: Pearls, Pitfalls, and Optimal Surgical Technique

2021· article· en· W3122420200 on OpenAlexfundno aff
Ajay C. Lall, Hari K. Ankem, Samantha C. Diulus, David R. Maldonado, Mitchell B. Meghpara, Philip J. Rosinsky, Jacob Shapira, Benjamin G. Domb

Bibliographic record

VenueArthroscopy Techniques · 2021
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsnot available
FundersGraymontMedwest AssociatesPacira PharmaceuticalsStrykerArthrex
KeywordsMedicineSurgery

Abstract

fetched live from OpenAlex

Treatment of hip joint chondral damage is a well-recognized aspect of the arthroscopic management of femoroacetabular impingement syndrome. Hip chondral resurfacing has evolved from microfracture to different forms of cartilage grafting, all with variable long-term outcomes. Recent literature has focused on techniques using different cartilage sources (native and synthetic products) that are available for clinicians to choose from during hip arthroscopy. None of the published reports on cartilage grafts have commented on hip joint capsular management as part of the procedure. This is likely because of the increased difficulty of capsular closure in the dry arthroscopic environment required for graft stabilization. However, potential iatrogenic hip instability induced by an unrepaired interportal capsulotomy can be detrimental to the existing joint architecture and possibly to the cartilage graft. This article presents a step-by-step approach, including tips and pearls, for capsular closure during arthroscopic acetabular chondral resurfacing with BioCartilage (Arthrex, Naples, FL). This method is a safe and reproducible way to close the joint capsule during chondral resurfacing in patients undergoing hip preservation that can potentially enhance the chances of a successful outcome.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.273
Teacher spread0.264 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueArthroscopy TechniquesSame topicHip disorders and treatmentsFrench-language works237,207