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Short Communication: Collagenated Cortico‐Cancellous Porcine Bone Grafts. A Study in Rabbit Maxillary Defects

2010· article· en· W1607710150 on OpenAlexvenueno aff
Ulf Nannmark, Iman Azarmehr

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2010
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsResorptionCancellous boneBone resorptionDentistryMaxillaBone formationMedicineBiomedical engineeringAnatomyPathologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Bone substitutes of collagenated porcine bone (CPB) have previously been shown to have osteoconductive properties and to be resorbed with time. The influence of different ratios between bone particles and collagen on bone response is not yet known. PURPOSE: The objective of the study was to evaluate the effect of different collagen ratios on the bone tissue responses to CPB grafts. MATERIALS AND METHODS: Eight rabbits were used in the study. Bilateral bone defects, 5 x 8 x 3 mm, were created in the maxilla and were filled with 60% CPB/40% collagen gel or with 80% CPB/20% collagen gel. Animals were killed after 8 weeks for histological and morphometrical evaluations. RESULTS: There were no differences between the two biomaterials tested. Both materials showed a high degree of bone formation, 42% and 46%, respectively. Both materials were showing signs of resorption at time of sacrifice. CONCLUSIONS: Different collagen/CPB ratios do not influence the bone tissue responses to CPB. Both materials exhibited osteoconductive properties and were starting to be resorbed at 8 weeks.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.108
GPT teacher head0.467
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2010
Admission routes1
Has abstractyes

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