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Record W4408081181 · doi:10.1111/ger.12758

Medication‐related osteonecrosis of the jaw in an older patient with multiple myeloma

2025· article· en· W4408081181 on OpenAlexaff
Larissa Couto de Freitas, V. Silva, Marta Miyazawa, Carine Ervolino de Oliveira, Felipe Fornias Sperandio, João Adolfo Costa Hanemann

Bibliographic record

VenueGerodontology · 2025
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsUniversity of Saskatchewan
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMedicineMultiple myelomaOsteonecrosis of the jawDentistryInternal medicineBisphosphonateOsteoporosis

Abstract

fetched live from OpenAlex

OBJECTIVE: This article reports a case of medication-related osteonecrosis of the jaw (MRONJ) associated with multiple myeloma (MM). BACKGROUND: Bisphosphonates (BPs) are constantly used as part of the treatment for MM. The main adverse effects of bisphosphonates are renal insufficiency and medication-related osteonecrosis of the jaw (MRONJ). MATERIALS AND METHODS: A 67-year-old female with a previous diagnosis of MM and undergoing current receiving intravenous injections of pamidronate underwent a tooth extraction and subsequently developed MRONJ. RESULTS: MRONJ was managed with clindamycin, surgical removal of bony sequestrum and curettage. At 3 years of follow-up, the patient was asymptomatic with no clinical changes and panoramic radiography without evidence of recurrence. CONCLUSION: Management of patients under antiresorptive drugs who require invasive dental procedures is challenging. Therefore, interprofessional collaboration, especially between the general practitioner and oncologist and/or haematologist, is essential to reach the best clinical approach and reduce the risk of MRONJ.

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.277
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations1
Published2025
Admission routes1
Has abstractyes

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