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Record W3136932791 · doi:10.1016/j.eats.2020.12.004

Technical Pearls for Arthroscopic Labral Augmentation of the Hip

2021· article· en· W3136932791 on OpenAlexfundno aff
Michael Scheidt, Daniel B. Haber, Sanjeev Bhatia, Michael B. Ellman

Bibliographic record

VenueArthroscopy Techniques · 2021
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignGraymontStryker
KeywordsMedicineLabrumSurgeryFemoroacetabular impingementPulleyHip arthroscopyFixation (population genetics)KiteArthroscopyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Our recent understanding of the importance of the acetabular labral suction seal has placed preserving labral integrity as a guiding principle in hip preservation surgery. In cases with a hypoplastic labrum and intact chondrolabral junction, labral augmentation presents as a viable alternative and an often preferred treatment option over labral reconstruction. At this time, there are few studies that have described the technical pearls of performing labral augmentation of the hip. In this technique guide, we describe, in detail, the kite technique for the introduction, control, and acetabular fixation of a hip labral augmentation graft. Comparable to flying a kite with 2 fly lines and to the previously described kite technique for hip labral reconstruction, the kite technique for labral augmentation is based on the principle that the use of 2 control sutures in a pulley system creates an efficient method to accurately and reproducibly facilitate graft passage and fixation during arthroscopic labral augmentation procedures.

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 score0.107
Threshold uncertainty score0.330

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 head0.018
GPT teacher head0.341
Teacher spread0.323 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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