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Record W3094219899 · doi:10.1016/j.ymgmr.2020.100661

Multigenerational case examples of hypophosphatasia: Challenges in genetic counseling and disease management

2020· article· en· W3094219899 on OpenAlexaff
Erin Huggins, Ricardo C. Ong, Cheryl R. Greenberg, Lauren B. Flueckinger, Kathryn Dahir, Priya S. Kishnani

Bibliographic record

VenueMolecular Genetics and Metabolism Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicAlkaline Phosphatase Research Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHypophosphatasiaGenetic counselingGeneticsDiseasePhenotypeGenetic testingInheritance (genetic algorithm)MedicineBioinformaticsGenetic heterogeneityBiologyGeneAlkaline phosphatasePathologyEnzymeBiochemistry

Abstract

fetched live from OpenAlex

gene. This leads to deficiency of tissue non-specific alkaline phosphatase (TNSALP), resulting in decreased mineralization of the bones and/or teeth and multi-systemic complications. Inheritance may be autosomal dominant or recessive, and the phenotypic spectrum, including age of onset, varies widely. We present four families demonstrating both modes of inheritance of HPP and phenotypic variability and discuss the resultant challenges in disease management, genetic counseling, and risk assessment. Failure to consider different modes of inheritance in a family with HPP may lead to an inaccurate risk assessment upon which medical and reproductive decisions may be made. We highlight the essential role of high-quality genetic counseling and meaningful biochemical and molecular testing strategies in the evaluation and management of families with HPP.

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)0.004
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.040
GPT teacher head0.286
Teacher spread0.246 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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