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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 OpenAlex
Erin Huggins, Ricardo C. Ong, Cheryl R. Greenberg, Lauren B. Flueckinger, Kathryn Dahir, Priya S. Kishnani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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