Letter to the Editor From Fitzpatrick et al: “Zoledronate After Denosumab Discontinuation: Is Repeated Administrations More Effective Than A Single Infusion?”
Bibliographic record
Abstract
Dear Editor, We read with interest the study by Grassi et al (1), which is the first to examine the application of European Calcified Tissue Society (ECTS) recommended bone turnover marker (BTM) cutoffs in guiding zoledronic acid therapy post denosumab cessation (2). The findings are very important as 75% of patients who stopped denosumab required a second infusion of zoledronic acid 6 months after the first (using a CTX cutoff >0.280 ng/L). Furthermore, despite 2 infusions there was significant loss of bone mineral density (BMD) at the lumbar spine (mean 5.4%) with 9.6% developing new fractures. This fracture incidence is concerning and more than anticipated. Notably, 2 patients with vertebral fractures had lumbar spine T scores of less than −3.0. All had prior vertebral fractures, but their recency was not documented. Additionally, one patient with an incident hip fracture with a T score of −4.1 at the neck of the femur was already at high risk of fracture. These 3 patients typically would not be advised to stop denosumab, and the decision to transition was driven by patient choice. It is concerning that one patient with a new vertebral fracture had a spine T score of −1.3, though their initial and follow-up CTX were respectively very high (1136 ng/mL) and high (413 ng/mL) with a large associated decline in spine BMD (10.0%). This supports the limited existing research (3, 4) highlighting the importance of BTMs during the “rebound period” in predicting BMD loss at the lumbar spine.
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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 | 0.004 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".