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Record W2132797493 · doi:10.1177/0363546506286866

Understanding and Preventing Noncontact Anterior Cruciate Ligament Injuries

2006· article· en· W2132797493 on OpenAlexaff
Letha Y. Griffin, Marjorie J. Albohm, Elizabeth A. Arendt, Roald Bahr, Bruce D. Beynnon, Marlene DeMaio, Randall W. Dick, Lars Engebretsen, William E. Garrett, Jo A. Hannafin, Timothy E. Hewett, Laura J. Huston, Mary Lloyd Ireland, Robert J. Johnson, Scott M. Lephart, Bert R. Mandelbaum, Barton J. Mann, Paul Marks, Stephen W. Marshall, Grethe Myklebust, Frank R. Noyes, Christopher M. Powers, Clarence L. Shields, Sandra J. Shultz, Holly J. Silvers, James R. Slauterbeck, Dean C. Taylor, Carol C. Teitz, Edward M. Wojtys, Bing Yu

Bibliographic record

VenueThe American Journal of Sports Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAnterior cruciate ligamentMedicineAnterior Cruciate Ligament InjuriesAthletesSports medicineAtlantaPhysical therapyPhysical medicine and rehabilitationSurgeryPathology

Abstract

fetched live from OpenAlex

The incidence of noncontact anterior cruciate ligament injuries in young to middle-aged athletes remains high. Despite early diagnosis and appropriate operative and nonoperative treatments, posttraumatic degenerative arthritis may develop. In a meeting in Atlanta, Georgia (January 2005), sponsored by the American Orthopaedic Society for Sports Medicine, a group of physicians, physical therapists, athletic trainers, biomechanists, epidemiologists, and other scientists interested in this area of research met to review current knowledge on risk factors associated with noncontact anterior cruciate ligament injuries, anterior cruciate ligament injury biomechanics, and existing anterior cruciate ligament prevention programs. This article reports on the presentations, discussions, and recommendations of this group.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.281
Teacher spread0.263 · 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 designTheoretical or conceptual
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

Citations1,046
Published2006
Admission routes1
Has abstractyes

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