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Record W2092032092 · doi:10.1080/15438620701877032

Exercises Following Anterior Cruciate Ligament Reconstructive Surgery: Biomechanical Considerations and Efficacy of Current Approaches

2008· review· en· W2092032092 on OpenAlexaff
Mark Grodski, Ray Marks

Bibliographic record

VenueResearch in Sports Medicine · 2008
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsOsteoporosis CanadaUniversity of Toronto
Fundersnot available
KeywordsAnterior cruciate ligamentMedicineContext (archaeology)CINAHLRehabilitationPhysical medicine and rehabilitationPhysical therapyAnterior Cruciate Ligament InjuriesKnee JointMEDLINEOsteoarthritisSurgeryAlternative medicine

Abstract

fetched live from OpenAlex

To enable a safe, effective return to daily functional activities and to prevent premature knee joint osteoarthritis, adults undergoing anterior cruciate ligament (ACL) surgery require carefully designed and appropriate rehabilitation strategies. In this article we critically examine the contemporary body of literature concerning the application and outcomes of various knee exercise rehabilitation protocols described in the literature in the context of the surgically repaired ACL injured knee. These data were obtained from the MEDLINE (1991-2007), CINAHL (1982-2007), and SPORTS DISCUS (1975-2007) databases, using the terms anterior cruciate ligament, exercise, and ligament, showed that two lines of research currently dominate this topic: (1) the role of open versus closed chain exercises approaches; (2) the impact of these exercises on knee stability and function post-ACL surgery. In light of the small number of studies and their methodological limitations, however, more research clearly is recommended to establish if closed-chain kinetic exercises are conclusively more efficacious than open-chain exercise for this condition, as predicted by the present literature base.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0040.005
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.296
GPT teacher head0.461
Teacher spread0.165 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations32
Published2008
Admission routes1
Has abstractyes

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