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Record W2036213901 · doi:10.1007/s11999-009-0735-8

Reconstruction of Complete Knee Extensor Mechanism Loss with Gastrocnemius Flaps

2009· article· en· W2036213901 on OpenAlexaff
Thilak Samuel Jepegnanam, P.R.J.V.C. Boopalan, Manasseh Nithyananth, V. T. K. Titus

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2009
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineMechanism (biology)Sports medicineSurgeryPhysical medicine and rehabilitationAnatomyOrthodonticsPhysical therapy

Abstract

fetched live from OpenAlex

UNLABELLED: We assessed the outcome after reconstruction of traumatic, complete, infected, extensor mechanism loss attributable to high-velocity open knee injuries in eight consecutive patients (all males) who presented to us between February 2005 and September 2007 at an average followup of 24 months. All were treated with gastrocnemius flaps. The loss in extensor mechanism was the patellar tendon in five patients, patella and patellar tendon in two patients, and combined patella, quadriceps, and patellar tendon in one patient. The size of the defect ranged from 8 x 5 cm to 15 x 15 cm. The patients were evaluated for functional outcome of the knee, resolution of infection, range of flexion of the knee, and return to work. Four patients had an excellent outcome whereas the others had a good outcome using the Hospital for Special Surgery knee rating scale. All flaps healed primarily with resolution of infection. The average knee flexion was 110 degrees. All patients except two returned to their original occupation. Three patients had an extensor lag of 5 degrees. The gastrocnemius flap is a good option for open knee injuries with extensor mechanism loss, giving consistent results across a wide spectrum of presentation. LEVEL OF EVIDENCE: Level IV, case series. See the Guidelines for Authors for a complete description of levels of evidence.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.058
GPT teacher head0.394
Teacher spread0.336 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations36
Published2009
Admission routes1
Has abstractyes

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