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Record W4376876012 · doi:10.1210/jcemcr/luad042

Multiple Spontaneous Vertebral Fractures in a Younger Post-menopausal Woman Upon Stopping Denosumab Therapy

2023· article· en· W4376876012 on OpenAlexaff
Leo Hu, William D. Leslie, Gregory Kline

Bibliographic record

VenueJCEM Case Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of ManitobaUniversity of Calgary
Fundersnot available
KeywordsDenosumabDiscontinuationMedicineOsteoporosisBone mineralBone remodelingSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Denosumab is a widely used medication for the treatment of osteoporosis. It has been observed in recent years that abruptly stopping denosumab leads to an increase in bone turnover markers, a decrease in bone mineral density, and a higher incidence of vertebral fractures. We present the case of a 53-year-old woman with few comorbidities and no prior fragility fractures who experienced 4 spontaneous and severely debilitating vertebral fractures 5-months post denosumab discontinuation. At the time of her fractures, she was found to have markedly elevated bone turnover markers, despite bone mineral density that was not significantly changed from measurements done while on denosumab treatment. She went on to be treated with an alternative antiresorptive agent, risedronate, and had substantial declines in her bone turnover markers, along with clinical improvement in her back pain. She experienced no further fractures while on treatment. Abrupt discontinuation of denosumab without starting an alternative antiresorptive agent can lead to spontaneous vertebral fractures. These fractures can occur in young patients with no prior history of fragility fractures and can be severely debilitating. An alternative antiresorptive agent should be started in the case of denosumab discontinuation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.211
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.342
Teacher spread0.313 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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