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Record W2885727235 · doi:10.2214/ajr.17.19334

MRI-Detected Sports-Related Knee Injuries and Abnormalities at the Rio de Janeiro 2016 Summer Olympic Games

2018· article· en· W2885727235 on OpenAlexaff
Andrew J. Kompel, Akira M. Murakami, Lars Engebretsen, Bruce B. Forster, Mina Lotfi, Mohamed Jarraya, Daichi Hayashi, Frank W. Roemer, M.D. Crema, Ali Guermazi

Bibliographic record

VenueAmerican Journal of Roentgenology · 2018
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of British Columbia
FundersInternational Olympic Committee
KeywordsMedicineSports injuryPhysical therapy

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this article is to describe knee abnormalities and the occurrence of MRI-detected sports-related knee abnormalities by evaluating MRI examinations performed during the Rio de Janeiro Olympic Games held in August 2016. CONCLUSION: There were 11,274 athletes at the Rio 2016 Olympic Games, and 113 of them underwent at least one knee MRI in the Olympic Village. Cartilage abnormalities, followed by meniscal tears and ligament sprains, were the most frequent abnormalities.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.257
Teacher spread0.251 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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