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Record W2968423766 · doi:10.1002/adhm.201900722

Treatment of Critical‐Sized Calvarial Defects in Rats with Preimplanted Transplants

2019· article· en· W2968423766 on OpenAlexafffund
Baptiste Charbonnier, Mohamed Abdulla, Andrew Gorgy, Hani Shash, Zishuai Zhang, Uwe Gbureck, Ed Harvey, Nicholas Makhoul, Mirko S. Gilardino, Jake E. Barralet

Bibliographic record

VenueAdvanced Healthcare Materials · 2019
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversité de MontréalMontreal General HospitalMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaPlastic Surgery Foundation
KeywordsMaterials scienceMedicine

Abstract

fetched live from OpenAlex

The local environment and the defect features have made the skull one of the most difficult regions to repair. Finding alternative strategies to repair large cranial defects, thereby avoiding the current limitations of autograft or polymeric and ceramic prostheses constitute an unmet need. In this study, the regeneration of an 8 mm critical-sized calvarial defect treated by autograft or by a monetite scaffold directly placed in the defect or preimplanted (either cranial bone transplant or subcutaneous pocket) and then transplanted within the bone defect is compared. The data reveal that transplantation of preimplanted monetite transplant scaffolds greatly improves the skull vault closure compared to subcutaneously preimplanted or directly placed materials. Autografts, while clearly filling the defect volume with bone appear effective since bone volume inside the defect volume is obviously high, but are not well fused to the skull. The preimplantation site has a large influence on the regeneration of the defect. Transplantation of induced bone inside materials has the potential to reduce the need for autograft harvest without damaging the skeleton. This first demonstration indicates that cranial repair may be possible without recourse to bioactives or cultured cell therapies.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.337
Teacher spread0.320 · 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 designBench or experimental
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

Citations10
Published2019
Admission routes2
Has abstractyes

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