Real-world efficacy and safety of burosumab in tumor-induced osteomalacia: case series from an early access program
Why this work is in the frame
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Bibliographic record
Abstract
Tumor-induced osteomalacia (TIO) is a rare paraneoplastic syndrome due to a phosphaturic tumor, which overproduces FGF23, causing hyperphosphaturia, hypophosphatemia, low 1,25(OH)2D, and osteomalacia. Complete surgical resection is the standard of care, but some tumors cannot be found, and others cannot be removed. In such difficult situations, burosumab, a fully human monoclonal antibody that targets and inhibits excess circulating FGF23, is a treatment option. Early access program (EAP) to burosumab has been established for patients with TIO in France in July 2022. Before that, access to burosumab at no cost on compassionate grounds was provided for a few patients. Between July 21, 2022 and December 3, 2023, an EAP was initiated for burosumab across 10 University Hospital Centers. The program included 9 patients (3 pre-exposed and 6 burosumab-naïve patients). The EAP included assessments of phosphatemia, pain levels using the visual analogue scale, and quality of life using the Routine Assessment of Patient Index Data 3 questionnaire. Patients' ages ranged from 33 to 62 yr, with various BMI categories. Seven patients had at least 1 follow-up visit (3 pre-exposed and 4 burosumab-naïve patients). In the burosumab-naïve group, phosphatemia levels improved in 2 patients, with 1 achieving levels >0.8 mmol/L. Pain reduction was reported in all 4 naïve patients with follow-up, while pain levels in pre-exposed patients remained stable or fluctuated. Quality of life scores indicated minimal impairment or remission in 6 patients at baseline. No serious adverse events were observed. These preliminary findings following burosumab EAP for patients with TIO in France support benefits in terms of efficacy, safety, and ease of treatment. Burosumab appears to be a promising option for patients who are ineligible or refractory to surgery.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it