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Record W2094321791 · doi:10.1007/s11999-008-0573-0

Revision of the Deficient Proximal Femur With a Proximal Femoral Allograft

2008· article· en· W2094321791 on OpenAlexaff
Oleg Safir, Catherine Kellett, M. Flint, David Backstein, Allan E. Gross

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2008
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineSurgeryFemurOrthopedic surgeryNonunionAcetabulumRadiographyArthroplasty

Abstract

fetched live from OpenAlex

UNLABELLED: Substantial bone loss is frequently encountered with revision hip arthroplasty. A proximal femoral allograft may be used to reconstitute bone stock in the multiply revised femur with segmental bone loss of greater than 5 cm. We retrospectively reviewed 92 patients (93 hips) who underwent such proximal femoral allografts. The average age at the surgery was 61 years. The average number of previous revision procedures was 2.5. Six patients were lost to followup. Thirty-four of 36 deceased patients had the original proximal femoral allograft at the time of death. The minimum followup for the 50 remaining patients was 15 years (average, 16.2 years; range, 15-22 years). Analysis included survivorship and radiographic assessment. Of the 50 patients reviewed, two had a failed reconstruction due to infection, six for aseptic loosening, three for nonunion, and four for dislocation. Revision of the proximal femoral allograft for all reasons excluding the acetabulum was performed in seven patients. At last followup, 42 patients (84%) had a well-functioning construct. Proximal femoral allograft for revision hip arthroplasty in femoral segmental bone loss is a durable alternative in most patients for a complex problem. LEVEL OF EVIDENCE: Level IV, therapeutic study. 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
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 head0.095
GPT teacher head0.387
Teacher spread0.291 · 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 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

Citations51
Published2008
Admission routes1
Has abstractyes

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