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Record W4412089525 · doi:10.1002/jeo2.70336

National Basketball Association combine scores as a predictive measure of lower limb surgery over 10 consecutive seasons (2010–2020): A retrospective review

2025· review· en· W4412089525 on OpenAlex
Rajal A. Patel, Rohan Shah, Tyler M. Hauer, Michael A. Terry, Vehniah K. Tjong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Experimental Orthopaedics · 2025
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBasketballMedicineAnkleLower limbOrthopedic surgeryRetrospective cohort studyPhysical therapyAnthropometrySports medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT Purpose The purpose of this study was to correlate National Basketball Association (NBA) Combine scores with future surgical lower limb injury to determine if NBA Combine scores can be predictive of future surgery on the lower limb. Methods A retrospective review of NBA surgical lower limb injuries was performed using a data set covering 10 consecutive NBA seasons (2010–2020). All NBA Combine data were obtained through the official NBA Combine website. NBA Combine data were matched to injury list and compared against noninjured control, described using means and standard deviations. Differences were evaluated using independent t ‐tests, with an a priori level of significance at p < 0.05. Results A total of 27,105 injury transactions were reported and a total of 130 players were identified who had undergone lower limb surgical management. There was no statistically significant difference in anthropometric stats (weight, body fat % and height). Lane agility time, three quarter sprint and max bench press showed no statistically significant differences. However, standing vertical leap and max vertical leap showed statistically significant differences, with values increased in injured group. Mean standing vertical was 73.86 cm (SD = 7.82) in noninjured and 76.00 cm (SD = 7.77) in the injured group. Mean max vertical was 86.89 cm (SD = 9.37) in noninjured and 89.31 cm (SD = 9.17) in injured group. Knee injuries (80.0%) were most prevalent, followed by ankle (8.5%), calf (7.7%), and thigh (3.8%). Knee surgeries comprised of general surgery on knee (42.3%), meniscal surgery (20.2%), arthroscopic knee surgery (18.3%), anterior cruciate ligament reconstruction (15.4%), and patellar tendon repair (3.8%). Conclusions Increased NBA Combine scores of standing and maximum vertical leap may be related to future lower limb injury requiring surgical management among basketball players. The knee remains the most injured joint with the majority of knee surgeries performed arthroscopically addressing meniscal pathology. Level of Evidence Level III.

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.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.260
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.341
Teacher spread0.322 · 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