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Record W2063105350 · doi:10.1097/moo.0b013e328304b445

Bisphosphonate-induced necrosis of the jaws: a reconstructive nightmare

2008· review· en· W2063105350 on OpenAlexaff
Artur Gevorgyan, Danny Enepekides

Bibliographic record

VenueCurrent Opinion in Otolaryngology & Head & Neck Surgery · 2008
Typereview
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoHealth Sciences Centre
Fundersnot available
KeywordsMedicineOsteoradionecrosisOsteonecrosis of the jawBisphosphonateSurgeryOsteoporosisDiseaseMultiple myelomaRadiation therapyBone Density Conservation AgentsMandible (arthropod mouthpart)DentistryBone densityInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Bisphosphonates are used for the management of metastatic bone disease, prevention and treatment of osteoporosis and Paget's disease of bone. An increasing number of reports have associated the use of bisphosphonates with the occurrence of osteoradionecrosis of the jaw. The purpose of this review is to summarize the recent literature in this area, specifically focusing on its management. RECENT FINDINGS: Osteoradionecrosis of the jaw mostly results from the use of intravenous bisphosphonates for metastatic bone disease and multiple myeloma; cases from oral treatment for osteoporosis and Paget's disease have also been reported. It mostly affects the posterior maxilla, followed by the posterior mandible. In more than half of the affected patients, there has been preceding dental trauma or procedure. Many treatment modalities have been attempted in order to limit disease progression. Conservative management with antibiotic therapy and mouthwashes seems to be most beneficial. Surgery should be reserved for refractory and symptomatic cases. SUMMARY: It is important for the reconstructive head and neck surgeon to recognize the clinical presentation of bisphosphonate-induced osteoradionecrosis of the jaw and the role of conservative management in consistently achieving good results.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.119
GPT teacher head0.387
Teacher spread0.268 · 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.

Study designNot applicable
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

Citations18
Published2008
Admission routes1
Has abstractyes

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