Hypophosphatasia diagnosis: current state of the art and proposed diagnostic criteria for children and adults
Bibliographic record
Abstract
BACKGROUND: This manuscript provides a summary of the current evidence to support the criteria for diagnosing a child or adult with hypophosphatasia (HPP). The diagnosis of HPP is made on the basis of integrating clinical features, laboratory profile, radiographic features of the condition, and DNA analysis identifying the presence of a pathogenic variant of the tissue nonspecific alkaline phosphatase gene (ALPL). Often, the diagnosis of HPP is significantly delayed in both adults and children, and updated diagnostic criteria are required to keep pace with our evolving understanding regarding the relationship between ALPL genotype and associated HPP clinical features. METHODS: An International Working Group (IWG) on HPP was formed, comprised of a multidisciplinary team of experts from Europe and North America with expertise in the diagnosis and management of patients with HPP. Methodologists (Romina Brignardello-Petersen and Gordon Guyatt) and their team supported the IWG and conducted systematic reviews following the GRADE methodology, and this provided the basis for the recommendations. RESULTS: The IWG completed systematic reviews of the literature, including case reports and expert opinion papers describing the phenotype of patients with HPP. The published data are largely retrospective and include a relatively small number of patients with this rare condition. It is anticipated that further knowledge will lead to improvement in the quality of genotype-phenotype reporting in this condition. CONCLUSION: Following consensus meetings, agreement was reached regarding the major and minor criteria that can assist in establishing a clinical diagnosis of HPP in adults and children.
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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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".