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Record W2012804639 · doi:10.1097/jsa.0b013e3182107d5f

Outcomes of Operative and Nonoperative Treatment of Multiligament Knee Injuries

2011· review· en· W2012804639 on OpenAlexaffabout
Christopher Peskun, Daniel B. Whelan

Bibliographic record

VenueSports Medicine and Arthroscopy Review · 2011
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineKnee DislocationNeurovascular bundleRange of motionOrthopedic surgeryConservative managementContractureMuscle contractureReturn to sportPhysical therapySurgeryRehabilitation

Abstract

fetched live from OpenAlex

Knee dislocation is an uncommon orthopedic diagnosis with a high rate of neurovascular complications. The goal of definitive management is to provide a pain free and functional knee through restoration of ligamentous stability and range of motion. Operative management has been suggested to be superior to nonoperative management for knee dislocations largely in part owing to a meta-analysis on the topic published a decade ago. The purpose of this study was to summarize the results of operative and nonoperative treatment of knee dislocations over the past 10 year period. There were a total of 855 patients from 31 studies managed operatively and 61 patients from 4 studies managed nonoperatively. The overall methodological quality of the studies was poor as measured by the Newstead-Ottawa scale. Data regarding functional outcome, instability, contracture, and return to activity were all in favor of operative management. Significant differences were found for return to employment (P<0.001) and return to sport (P=0.001). The results of this study provide further evidence for the superiority of operative management, compared with nonoperative management, for knee dislocations across several clinical and functional domains. There is a need for higher level studies to assist the treating surgeon in the management of these challenging injuries.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.932
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.414
Teacher spread0.359 · 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.

Study designOther design
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

Citations148
Published2011
Admission routes2
Has abstractyes

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