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Record W4251465192 · doi:10.1177/2380084417701897

Osseointegration Pharmacology

2017· article· en· W4251465192 on OpenAlexafffund
Faleh Tamimi, Xing Wu

Bibliographic record

VenueJDR Clinical & Translational Research · 2017
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsMcGill University
FundersInstitute of Musculoskeletal Health and ArthritisITI FoundationChina Scholarship CouncilCanadian Institutes of Health ResearchAO FoundationRéseau de Recherche en Santé Buccodentaire et Osseuse
KeywordsOsseointegrationImplantMedicineDentistryBone remodelingBone healingSurgeryInternal medicine

Abstract

fetched live from OpenAlex

The process of osseointegration around dental implants is similar to the biological events occurring during bone repair and fracture healing. Therefore, bone metabolic activity plays a crucial role on the success of osseointegration, and dysregulation of bone metabolism can have a negative impact on bone healing and implant osseointegration. Accordingly, it could be hypothesized that drugs interfering with healing and bone metabolism could affect osseointegration and implant survival. Looking into the relationship between pharmacology, osseointegration, and dental implants, drugs can open the door for new pharmacological innovations to improve implant success and avoid unnecessary complications, and it is also of special interest because most implant patients are elder adults who are often polymedicated. In this commentary, we discuss the discoveries made by us as well as by other researchers regarding the effect of several drugs on bone, osseointegration, and implant survival. Of particular interest is the growing evidence showing that commonly used drugs such as nonsteroidal anti-inflammatories, serotonin reuptake inhibitors, and proton pump inhibitors could lead to implant failure.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.503
GPT teacher head0.654
Teacher spread0.151 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2017
Admission routes2
Has abstractyes

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