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Record W1916722083 · doi:10.1111/etp.12017

Healing of large dentofacial defects

2011· article· en· W1916722083 on OpenAlexaff
George K.B. Sándor, Robert P. Carmichael, Leena P. Ylikontiola, Ahmed Jan, Marc G. Duval, Cameron M. L. Clokie

Bibliographic record

VenueEndodontic Topics · 2011
Typearticle
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsUniversity of TorontoHolland Bloorview Kids Rehabilitation Hospital
FundersStryker
KeywordsVascularityRegeneration (biology)Hard tissueSoft tissueAdipose tissueMedicineTissue engineeringComputer scienceOrthodonticsBiomedical engineeringDentistrySurgeryBiologyCell biology

Abstract

fetched live from OpenAlex

Dentofacial defects can be small or very large, consisting of defects in the craniomaxillofacial region with missing soft tissue, bony and other hard tissue components. Such combined mucosal, osseous and even cartilaginous defects can be reconstructed using flaps and bone grafts, or hopefully, in the future with bone graft substitutes or even tissue engineered constructs. The healing of such wounds always relies on the vascularity of the surrounding tissues. This chapter seeks to provide a physiological basis for the mechanisms involved in the healing of such large complex defects. The reconstruction of specific defects must follow sound and logical surgical principles. The authors employ the concept of the reconstructive surgical ladder, in which techniques of step‐wise increasing complexity are used with a strong preference for the simplest possible procedure at the outset. A number of techniques are presented along with the principles of tissue engineering and the basis for bone regeneration using adipose derived stem cells, growth factors and resorbable scaffolds.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.331

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 head0.044
GPT teacher head0.298
Teacher spread0.254 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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