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Record W2986753708 · doi:10.1007/s11420-019-09736-5

Impact of Knee Injuries on Post-retirement Pain and Quality of Life: A Cross-Sectional Survey of Professional Basketball Players

2019· article· en· W2986753708 on OpenAlexaff
Moin Khan, Seper Ekhtiari, Tyrrell Burrus, Kim Madden, Joseph P. Rogowski, Asheesh Bedi

Bibliographic record

VenueHSS Journal® The Musculoskeletal Journal of Hospital for Special Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBasketballMedicineAthletesPhysical therapyCross-sectional studyKnee painSports medicineDemographicsQuality of life (healthcare)NursingDemographyOsteoarthritisAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Professional basketball players are at increased risk for knee injuries. Epidemiologic data exist on the prevalence of such injuries in players in the National Basketball Association (NBA), but little is known about how these injuries affect athletes before after retirement. QUESTIONS/PURPOSES: The goals of this study were to evaluate the rates and characteristics of knee injury before and during NBA players' careers and how those injuries correspond to knee injury, pain, or surgery, as well as quality of life, after retirement. METHODS: A cross-sectional survey study was performed. The survey instrument was designed with the aid of a multidisciplinary focus group. Data collected included patient demographics; length of professional career; injuries before, during, and after the athletes' NBA careers; and post-retirement quality of life, assessed using the EQ-5D and Tegner Activity Scale. The survey was distributed electronically to 900 retired NBA athletes. Descriptive statistics were used to present means and proportions, and multiple regression analysis was performed to assess for potential factors correlated to injury. RESULTS: One hundred eight retired NBA players participated (a response rate of 12%). Almost a third (32.4%) sustained a knee injury before starting their NBA career; 51 (47.2%) sustained knee injury during professional play in the NBA, and nearly two-thirds of those players (62.7%) needed surgery. Among those who reported knee injuries during their NBA career, a majority had knee pain that continued until retirement (72.5%). Two-thirds (67%) reported having knee pain currently (at the time of the survey). More than a third (34.0%) underwent knee surgery after retirement, which included nine total knee arthroplasties (8.3%). CONCLUSION: A majority of retired NBA athletes in our study had knee pain, and many needed operative management during and after their NBA careers. NBA players score lower on quality-of-life measures than average North American men of similar age. Further research is needed to elucidate the best strategies for recognizing and treating knee injuries in these athletes.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.356
Teacher spread0.330 · 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 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

Citations11
Published2019
Admission routes1
Has abstractyes

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