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Record W2086251430 · doi:10.1902/jop.2005.76.4.621

Maintenance of Osseointegration Utilizing Insulin Therapy in a Diabetic Rat Model

2005· article· en· W2086251430 on OpenAlexaff
Peter Kwon, Suraiya S. Rahman, David M. Kim, Jeffrey A. Kopman, Nadeem Y. Karimbux, Joseph P. Fiorellini

Bibliographic record

VenueJournal of Periodontology · 2005
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsInstitute of Infection and Immunity
FundersITI Foundation
KeywordsOsseointegrationMedicineInsulinDiabetes mellitusDentistryInternal medicineEndocrinologySurgeryImplant

Abstract

fetched live from OpenAlex

BACKGROUND: Normal wound healing processes have been shown to be altered in diabetes, and the effect of the diabetes on bone-to-implant contact (BIC) once osseointegration has been established is still unknown. The purpose of this study was to histologically evaluate the bone-to-implant contact in uncontrolled and insulin-controlled rats in which diabetes was induced following the establishment of osseointegration. METHODS: Thirty-two rats were assigned to eight different treatment groups of four each. Titanium plasma-sprayed (TPS) implants were placed in the femora of each animal, and allowed to osseointegrate for 28 days before diabetic induction. Daily insulin injections were given to four groups of rats and the other four groups received no insulin (uncontrolled). The rats were sacrificed at 1, 2, 3, and 4 months following diabetic induction. RESULTS: The results indicated that at 1, 2, 3, and 4 months, there was more BIC in the insulin-controlled groups compared to the uncontrolled groups. The differences were significantly greater at 2, 3, and 4 months (P < or =0.001). CONCLUSIONS: This study demonstrated that osseointegrated dental implants in insulin-controlled diabetic rats maintained bone-to-implant contacts over a 4-month period. However, boneto- implant contact appears to decrease with time in uncontrolled diabetic rats.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.341

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 head0.033
GPT teacher head0.317
Teacher spread0.284 · 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

Citations52
Published2005
Admission routes1
Has abstractyes

Explore more

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