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Record W3046874021 · doi:10.1177/2325967120s00418

Predictors of Large Labral Tears: A Multicenter Orthopaedic Outcomes Network (MOON) Shoulder Instability Cohort Study

2020· article· en· W3046874021 on OpenAlexaboutno aff
Kevin Cronin, Brian R. Wolf, Justin A. Magnuson, Gregory S. Hawk, Azimeh Sedaghat, Katherine Thompson, Shannon F. Ortiz, Cale A. Jacobs

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLabrumTearsRotator cuffSurgeryCohortExact testArthroscopyInternal medicine

Abstract

fetched live from OpenAlex

Objectives: Labral tears are often described by either their location (superior, anterior, or posterior) or their size, commonly defined as degrees of labral involvement from 0° to 360°. Large tears are thought to include 270° or more of the labrum, which has been reported to include 3.3% to 6.5% of those undergoing shoulder instability surgery for labral pathology. Demographic or injury characteristics of those with large labral tears (>270°) has not been defined in the literature. The purpose of this study was to identify factors predictive of a large labral tear at the time of shoulder instability surgery. Methods: As part of the Multicenter Orthopaedic Outcomes Network (MOON) Shoulder Instability cohort, patients undergoing open or arthroscopic shoulder instability surgery for a labral tear were evaluated. Those with an isolated SLAP (superior labrum anterior to posterior) tear or a concomitant rotator cuff tear requiring repair were excluded. Demographic data, injury history, preoperative patient-reported outcome scores (PROs), imaging and intraoperative findings, and surgical procedures performed were recorded. The treating surgeon reported the size and location of labral pathology visualized at the time of surgery. Patients with greater than a 270° tear were defined as having a large labral tear. For categorical demographic variables, a chi-square test or Fisher’s Exact test was used, as appropriate based on cell counts. For continuous demographic variables, a two-sample t-test was performed. In order to build a predictive logistic regression model for large tears, the Feasible Solutions Algorithm (Lambert et al. 2018) was used to add significant interaction effects iteratively until no more significant two-way interactions could be added to the model. Results: After applying exclusion criteria, 1235 patients were available for analysis. There were 222 females (18.0%) and 1013 males (82.0%) in the cohort with an average age of 24.7 years old (12 – 66 years old). The incidence of large tears was 4.6% with the average tear size being 141.9°, or 39.4%. Males accounted for significantly more of the large tears seen in the cohort (94.7%, p = 0.01). Racquet sports (p = 0.002), swimming (p = 0.02), softball (p = 0.05), skiing (p = 0.04), and golf (p = 0.04) were all found to be predictive of large labral tears as was a higher Western Ontario Shoulder Instability (WOSI) score (p = 0.01) (Table 1). Patients with a larger body mass index (BMI) who played contact sports were also more likely to have large tears (p = 0.007). Age, race, history of dislocation, injury during sport, or previous shoulder surgery were not associated with having a larger tear. Conclusion: Patients with large labral tears are a small, but not insignificant, subset of patients undergoing shoulder instability surgery. Multiple factors were identified as being associated with large labral tears at the time of surgery including male sex, pre-operative WOSI score, and participation in certain sports including racquet sports, softball, skiing, swimming, and golf. Surgeons treating patients with these risk factors should be prepared to encounter a large labral tear at the time of surgery. Further studies will evaluate the outcomes of this patient population. [Table: see text]

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.304
Teacher spread0.284 · 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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Citations1
Published2020
Admission routes1
Has abstractyes

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