Multiple Spontaneous Vertebral Fractures in a Younger Post-menopausal Woman Upon Stopping Denosumab Therapy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".