The Global Hypophosphatasia Registry: lessons learned from a decade of real-world data
Bibliographic record
Abstract
INTRODUCTION: Hypophosphatasia (HPP) is an inherited, metabolic, rare disease characterized by a high level of clinical heterogeneity. In response to this robust heterogeneity, the Global HPP Registry was formed to characterize the types of manifestations that patients may experience, as well as to compile information on genetic underpinnings of the disease, overall impact on patient quality of life, and safety and effectiveness of enzyme replacement therapy. The objective of this review was to synthesize key learnings gained from the Global HPP Registry, which is now in its tenth year of enrolling patients. METHODS: Registry data were analyzed to provide up-to-date information on age at diagnosis of HPP and alkaline phosphatase substrate testing. Published articles and abstracts reporting results from the registry were reviewed and summarized. RESULTS: Analyses showed peaks in age at diagnosis of HPP in early childhood and middle adulthood. Pyridoxal 5'-phosphate testing was performed in 18% to 61% of registry patients across geographic regions, and phosphoethanolamine testing was performed in 5% to 48% of registry patients. Published reports demonstrate that nonskeletal manifestations of HPP are an important disease feature that can affect functional outcomes. The review also reports recent findings on the genetics of HPP across a broad patient population, including heterozygous patients, and integrated literature showing that patients with HPP can have high levels of disease burden regardless of whether they present with overt skeletal manifestations or if the disease first presents in childhood or adulthood. Based on the collective findings of this review, an updated classification system for patients with HPP is proposed that incorporates a more recent understanding of the spectrum of this condition. Outcomes showing the effectiveness of enzyme replacement therapy among children and adults treated in a real-world setting are also included. CONCLUSIONS: In summary, learnings from the past decade of the registry have improved the overall understanding of HPP in a wide patient population and may play an important role in improving disease recognition and diagnosis.
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 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 | 0.113 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.025 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".