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Record W2529655959 · doi:10.1177/2326409816669375

Expert Opinions on Managing Fertility and Pregnancy in Patients With Mucopolysaccharidosis

2016· article· en· W2529655959 on OpenAlexaff
Fiona Stewart, Andrew Bentley, Barbara K. Burton, Nathalie Guffon, Susan Hale, Paul Harmatz, Susanne Gerit Kircher, Pavan Kochhar, John J. Mitchell, Ursula Plöckinger, Jennifer Semotok, S.C. Graham, Stephen Sande, Zlatko Sisic, Tracey Johnston

Bibliographic record

VenueJournal of Inborn Errors of Metabolism and Screening · 2016
Typearticle
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsUniversity Health NetworkMontreal Children's Hospital
FundersBioMarin Pharmaceutical
KeywordsFertilityPregnancyMultidisciplinary approachMedicineMucopolysaccharidosisIntensive care medicinePopulationPediatricsMultidisciplinary teamPathologyNursingBiologyPolitical scienceEnvironmental health

Abstract

fetched live from OpenAlex

The mucopolysaccharidosis (MPS) disorders are rare genetic diseases caused by deficiencies in lysosomal enzymes involved in the degradation of glycosaminoglycans, leading to pulmonary, cardiac and neurological dysfunctions, skeletal anomalies, impaired vision, and/or hearing and shortened life spans. Whereas in the past, few individuals with MPS reached adulthood, better diagnosis, multidisciplinary care, and new therapies have led to an increasing number of adult patients with MPS. Therefore, fertility and pregnancy questions in this patient population are becoming more important. Management of fertility issues and pregnancy in patients with MPS is challenging due to the lack of documented cases and a dearth in the literature on this topic. This review presents multidisciplinary expert opinions on managing fertility and pregnancy based on case studies and clinical experience presented at a meeting of MPS specialists held in Berlin, Germany, in April 2015. An overview of the existing literature on this subject is also included.

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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.292

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 head0.022
GPT teacher head0.284
Teacher spread0.262 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Inborn Errors of Metabolism and ScreeningSame topicLysosomal Storage Disorders ResearchFrench-language works237,207