Item Response Theory Quantifies the Relationship Between Improvements in Serum Phosphate and Patient‐Reported Outcomes in Adults With X‐Linked Hypophosphatemia
Bibliographic record
Abstract
Burosumab is indicated for treatment of a rare bone disease, X-linked hypophosphatemia (XLH). The aim of this analysis was to evaluate the relationship between a treatment response biomarker and patient-reported outcomes (PROs). Longitudinal data for PROs were obtained from adults with XLH from a phase III study. Individual rich time profiles of the biomarker, serum phosphate were simulated using a prior population pharmacokinetic-pharmacodynamic model to calculate serum phosphate exposure metrics for each 28-day treatment cycle, which were then merged with PROs data. Item response theory parameters were first estimated to map a latent variable, ψ, that is, disability score, relative to baseline. Next, the relationships between serum phosphate exposures and ψ were modeled using a nonlinear mixed-effect (NLME) modeling approach. A combined item response theory-NLME model with average serum phosphate as a predictor of ψ described PROs data well. The model estimates suggested 28%, 31%, and 25% reduction in Western Ontario and McMaster Universities Osteoarthritis Index, brief pain inventory, and brief fatigue inventory scores, respectively, with every unit increase in average serum phosphate from the lower limit of normal (2.5 mg/dL). Additionally, a time effect of ~ 0.08% improvements each week was estimated. The analysis suggested that burosumab treatment-induced improvements in serum phosphate levels are associated with improvements in PROs in adults with X-linked hypophosphatemia. The analyses confirmed the importance of prolonged serum phosphate level correction in adult patients with XLH. These results can be useful to guide the design of further studies and to design treatment optimization strategies.
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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 | 0.033 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".