MétaCan
Menu
Back to cohort
Record W2568988036 · doi:10.1177/2325967116676269

Mean Glenoid Defect Size and Location Associated With Anterior Shoulder Instability

2017· article· en· W2568988036 on OpenAlexaff
Lionel Gottschalk, Aaron J. Bois, Marcus Shelby, Anthony Miniaci, Morgan H. Jones

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineAnterior shoulderInstabilityJoint instabilityOrthodonticsSurgeryMechanics

Abstract

fetched live from OpenAlex

BACKGROUND: There is a strong correlation between glenoid defect size and recurrent anterior shoulder instability. A better understanding of glenoid defects could lead to improved treatments and outcomes. PURPOSE: To (1) determine the rate of reporting numeric measurements for glenoid defect size, (2) determine the consistency of glenoid defect size and location reported within the literature, (3) define the typical size and location of glenoid defects, and (4) determine whether a correlation exists between defect size and treatment outcome. STUDY DESIGN: Systematic review; Level of evidence, 4. METHODS: PubMed, Ovid, and Cochrane databases were searched for clinical studies measuring glenoid defect size or location. We excluded studies with defect size requirements or pathology other than anterior instability and studies that included patients with known prior surgery. Our search produced 83 studies; 38 studies provided numeric measurements for glenoid defect size and 2 for defect location. RESULTS: From 1981 to 2000, a total of 5.6% (1 of 18) of the studies reported numeric measurements for glenoid defect size; from 2001 to 2014, the rate of reporting glenoid defects increased to 58.7% (37 of 63). Fourteen studies (n = 1363 shoulders) reported defect size ranges for percentage loss of glenoid width, and 9 studies (n = 570 shoulders) reported defect size ranges for percentage loss of glenoid surface area. According to 2 studies, the mean glenoid defect orientation was pointing toward the 3:01 and 3:20 positions on the glenoid clock face. CONCLUSION: Since 2001, the rate of reporting numeric measurements for glenoid defect size was only 58.7%. Among studies reporting the percentage loss of glenoid width, 23.6% of shoulders had a defect between 10% and 25%, and among studies reporting the percentage loss of glenoid surface area, 44.7% of shoulders had a defect between 5% and 20%. There is significant variability in the way glenoid bone loss is measured, calculated, and reported.

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)0.011
metaresearch head score (Gemma)0.080
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.080
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0100.009
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.297
Teacher spread0.278 · 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".

Quick stats

Citations21
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueOrthopaedic Journal of Sports MedicineSame topicShoulder Injury and TreatmentFrench-language works237,207