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Record W4412447125 · doi:10.1016/j.bonr.2025.101857

Multigenerational genetic inheritance and clinical characteristics of the rare disease hypophosphatasia in 6 families: A case series

2025· article· en· W4412447125 on OpenAlexaff
Pekka Kannus, Aliya Khan, Mira Francis, Jonathan D. Adachi

Bibliographic record

VenueBone Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicAlkaline Phosphatase Research Studies
Canadian institutionsMcMaster UniversityAstraZeneca (Canada)University of Alberta
FundersAstraZeneca
KeywordsHypophosphatasiaInheritance (genetic algorithm)DiseaseGeneticsBiologyMedicinePathologyGeneAlkaline phosphatase

Abstract

fetched live from OpenAlex

Family mapping is a useful tool for tracking the inheritance of rare inherited diseases, including hypophosphatasia (HPP), through generations. We show the inheritance of HPP in 6 affected families, describing genetic variants, biochemical hallmarks, and clinical manifestations among family members. Mapping families with HPP is warranted in clinical practice to better understand monitoring needs for potentially affected individuals over time, since manifestations of HPP can arise throughout a patient's lifespan.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.020
GPT teacher head0.333
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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