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Record W1996324943 · doi:10.1016/j.injury.2009.06.288

A biodegradable scaffold for the treatment of a diaphyseal bone defect of the tibia

2009· article· en· W1996324943 on OpenAlexaff
Paul R.T. Kuzyk, E. Schemitsch, J. E. Davies

Bibliographic record

VenueInjury Extra · 2009
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineScaffoldTibiaDentistryOrthodonticsAnatomyBiomedical engineering

Abstract

fetched live from OpenAlex

The aim of this study was to compare angiogenesis and osteogenesis occurring within 8.0 mm diaphyseal defects created in canine tibiae treated using autograft or a biodegradable bone scaffold. All tibiae were reamed to 7.0 mm and fixed with a 6.5-mm statically locked intramedullary nail. Each of the 18 canines as allotted to one of three treatment groups: (1) left empty (N = 5), (2) treated with iliac crest autograft (N = 6), or (3) treated with a PLGA/calcium phosphate biodegradable scaffold (N = 7). Fluorescent markers were given at successive time periods: calcein green at 6 weeks, xylenol orange at 9 weeks, and tetracycline at 11 and 14 weeks. Animals were sacrificed at 15 weeks and their legs were perfused with a radio opaque compound. Samples were analyzed using Micro CT, bright-field microscopy and fluorescent microscopy. Scaffold samples were found to have significantly greater bone formation (p = 0.015) and blood vessel formation (p < 0.001) at their osteotomy sites than autograft samples. Bone formation rate in the periosteum was significantly greater in the autograft samples than the scaffold samples for all time periods. Bone formation at the osteotomy site was found to be significantly greater when associated with greater blood vessel formation (p = 0.026). The PLGA/calcium phosphate biodegradable scaffold we have employed supports angiogenesis within a segmental tibial defect that has adequate soft tissue coverage.

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 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 score0.839
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.020
GPT teacher head0.294
Teacher spread0.274 · 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

Citations7
Published2009
Admission routes1
Has abstractyes

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