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Management of Bone Loss

2006· article· en· W2084388516 on OpenAlexaff
David Backstein, Oleg Safir, Allan E. Gross

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2006
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicinePeriprostheticNonunionSurgeryAseptic processingFixation (population genetics)RadiographyFemurImplantArthroplasty

Abstract

fetched live from OpenAlex

UNLABELLED: Massive bone defects are challenging problems in revision knee surgery. When defects are large and uncontained (without a cortical rim), structural allografts may be used to provide support for femoral and tibial components. This study reviewed 68 structural allografts at a mean of 5.4 years for clinical and radiographic outcomes. Indications for grafts included periprosthetic fracture in 19 knees, aseptic loosening in 29, infection in 11 and instability in 2. Seven knees had both femoral and tibial allografts. Multiple implant designs were used including 7 hinged prostheses. Thirteen knees (13/61) failed due to graft related complications including one graft nonunion, three aseptic loosenings, three periprosthetic fractures, four infections, and two for instability. The case of graft nonunion was successfully treated with revision fixation and autologous bone graft. There were three cases of graft resorption, two graded as severe and one as moderate. These results are satisfactory given the nature and complexity of the problem, however, reconstructive procedures require careful preoperative preparation and extensive experience in complex knee arthroplasty. LEVEL OF EVIDENCE: Therapeutic study, level IV (case series). See 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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.415
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.

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

Citations143
Published2006
Admission routes1
Has abstractyes

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