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Record W3213206229 · doi:10.1177/03635465211059195

Anterior Shoulder Instability in Throwers and Overhead Athletes: Long-term Outcomes in a Geographic Cohort

2021· article· en· W3213206229 on OpenAlexaboutno aff
Ryan R. Wilbur, Matthew B. Shirley, Richard F. Nauert, Matthew D. LaPrade, Kelechi R. Okoroha, Aaron J. Krych, Christopher L. Camp

Bibliographic record

VenueThe American Journal of Sports Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesStrykerAmerican Orthopaedic Society for Sports MedicineMusculoskeletal Transplant FoundationHistogenicsArthrex
KeywordsMedicineAthletesCohortThrowingPhysical therapyCohort studyPopulationInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Background: Athletes of all sports often have shoulder instability, most commonly as anterior shoulder instability (ASI). For overhead athletes (OHAs) and those participating in throwing sports, clinical and surgical decision making can be difficult owing to a lack of long-term outcome studies in this population of athletes. Purpose/Hypothesis: To report presentation characteristics, pathology, treatment strategies, and outcomes of ASI in OHAs and throwers in a geographic cohort. We hypothesized that OHAs and throwers would have similar presenting characteristics, management strategies, and clinical outcomes but lower rates of return to play (RTP) when compared with non-OHAs (NOHAs) and nonthrowers, respectively. Study Design: Cohort study; Level of evidence, 3. Methods: An established geographic medical record system was used to identify OHAs diagnosed with ASI in the dominant shoulder. An overall 57 OHAs with ASI were matched 1:2 with 114 NOHAs with ASI. Of the OHAs, 40 were throwers. Sports considered overhead were volleyball, swimming, racquet sports, baseball, and softball, while baseball and softball composed the thrower subgroup. Records were reviewed for patient characteristics, type of sport, imaging findings, treatment strategies, and surgical details. Patients were contacted to collect Western Ontario Shoulder Instability index (WOSI) scores and RTP data. Statistical analysis compared throwers with nonthrowers and OHAs with NOHAs. Results: Four patients, 3 NOHAs and 1 thrower, were lost to follow-up at 6 months. Clinical follow-up for the remaining 167 patients (98%) was 11.9 ± 7.2 years (mean ± SD). Of the 171 patients included, an overall 41 (36%) NOHAs, 29 (51%) OHAs, and 22 (55%) throwers were able to be contacted for WOSI scores and RTP data. OHAs were more likely to initially present with subluxations (56%; P = .030). NOHAs were more likely to have dislocations (80%; P = .018). The number of instability events at presentation was similar. OHAs were more likely to undergo initial operative management. Differences in rates of recurrent instability were not significant after initial nonoperative management (NOHAs, 37.1% vs OHAs, 28.6% [ P = .331] and throwers, 21.2% [ P = .094]) and surgery (NOHAs, 20.5% vs OHAs, 13.0% [ P = .516] and throwers, 9.1% [ P = .662]). Rates of revision surgery were similar (NOHAs, 18.0% vs OHAs, 8.7% [ P = .464] and throwers, 18.2% [ P > .999]). RTP rates were 80.5% in NOHAs, as compared with 71.4% in OHAs ( P = .381) and 63.6% in throwers ( P = .143). Median WOSI scores were 40 for NOHAs, as compared with 28 in OHAs ( P = .425) and 28 in throwers ( P = .615). Conclusion: In a 1:2 matched comparison of general population athletes, throwers and OHAs were more likely to have more subtle instability, as evidenced by higher rates of subluxations rather than frank dislocations, when compared with NOHAs. Despite differences in presentation and the unique sport demands of OHAs, rates of recurrent instability and revision surgery were similar across groups. Similar outcomes in terms of RTP, level of RTP, and WOSI scores were achieved for OHAs and NOHAs, but these results must be interpreted with caution given the limited sample size.

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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 score0.011
Threshold uncertainty score0.022

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)0.0010.000

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 head0.328
Teacher spread0.308 · 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

Citations20
Published2021
Admission routes1
Has abstractyes

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