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Record W2514245972 · doi:10.1177/2326409816666298

A 15-Year Perspective of the Fabry Outcome Survey

2016· article· en· W2514245972 on OpenAlexaff
Roberto Giugliani, Dau‐Ming Niu, Uma Ramaswami, Michael L. West, Derralynn Hughes, Christoph Kampmann, Guillem Pintos‐Morell, Kathy Nicholls, Jörn-Magnus Schenk, Michael Beck

Bibliographic record

VenueJournal of Inborn Errors of Metabolism and Screening · 2016
Typearticle
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNatural historyShireFabry diseaseMedicineObservational studyPerspective (graphical)DiseaseIntensive care medicineIncidence (geometry)PediatricsInternal medicine

Abstract

fetched live from OpenAlex

The Fabry Outcome Survey (FOS) is an international long-term observational registry sponsored by Shire for patients diagnosed with Fabry disease who are receiving or are candidates for therapy with agalsidase alfa (agalα). Established in 2001, FOS provides long-term data on agalα safety/efficacy and collects data on the natural history of Fabry disease, with the aim of improving clinical management. The FOS publications have helped establish prognostic and severity scores, defined the incidence of specific disease variants and implications for clinical management, described clinical manifestations in special populations, confirmed the high prevalence of cardiac morbidity, and demonstrated correlations between ocular changes and Fabry disease severity. These FOS data represent a rich resource with utility not only for description of natural history/therapeutic effects but also for exploratory hypothesis testing and generation of tools for diagnosis/management, with the potential to improve future patient outcomes.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.021
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.052
GPT teacher head0.334
Teacher spread0.282 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

Explore more

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