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Record W2101225967 · doi:10.1902/jop.2002.73.7.813

Bisphosphonates and Periodontics: Potential Applications for Regulation of Bone Mass in the Periodontium and Other Therapeutic/ Diagnostic Uses

2002· review· en· W2101225967 on OpenAlexaff
Howard C. Tenenbaum, Avi Shelemay, Bruno Girard, Ron Zohar, Péter Fritz

Bibliographic record

VenueJournal of Periodontology · 2002
Typereview
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPeriodontologyPeriodontiumMedicineBone resorptionDentistryOsteoclastBisphosphonateBone Density Conservation AgentsOsteoporosisBone remodelingMechanism (biology)PharmacologyBioinformaticsPathologyInternal medicineBone mineralBiology

Abstract

fetched live from OpenAlex

Bisphosphonates are widely utilized in the management of systemic metabolic bone disease due to their ability to inhibit bone resorption. Recently, new uses of this unique class of pharmacological agents have been suggested. Given their known affinity to bone and their ability to increase osteoblastic differentiation and inhibit osteoclast recruitment and activity, there exists a possible use for bisphosphonates in the diagnosis and management of periodontal diseases. These bone-specific properties could also provide an interesting management strategy to stimulate osteogenesis in conjunction with regenerative materials around osseous defects and may also result in the promotion of bone formation around endosseous implants. The objective of this article is to review the scientific evidence regarding the potential applications of bisphosphonate drugs in the therapeutic management of periodontal diseases. Moreover, the mechanism of action and the pharmacology of these drugs will be reviewed. Finally, the potential role of bisphosphonates regarding their potential to accelerate bone formation, in addition to their usual uses for inhibition of bone resorption, is discussed.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.047
GPT teacher head0.349
Teacher spread0.301 · 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 designOther design
Domainnot available
GenreReview

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

Citations88
Published2002
Admission routes1
Has abstractyes

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