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A systematic review on safety and efficacy of Migalastat for the treatment of Fabry’s disease

2024· article· en· W6901898800 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typearticle
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsnot available
Fundersnot available
KeywordsDiseaseAdverse effectMEDLINEClinical trialScale (ratio)

Abstract

fetched live from OpenAlex

Fabry’s disease (FD) is a genetic lysosomal storage disorder characterized by α-galactosidase A (α-Gal A) lost/reduced activity. We aim to systematically assess the safety and efficacy of Migalastat, an oral pharmacological chaperone, that has been approved for the treatment of FD in patients with amenable mutations. We conducted literature search following the PRISMA guidelines in major databases up to 4 February 2024, for studies that assessed the clinical outcomes of migalastat in patients with FD. The New Castle Ottawa Scale was used to evaluate the quality of the included studies. A total of 2141 records were identified through database searches and register searches, amongst which 30 records were screened, and 12 of these were excluded. The remaining 20 reports were sought for retrieval. The 12 retrieved articles were assessed for eligibility and their quality was assessed. Amongst the included studies, 5 were of high quality, 6 were of medium quality, and 1 was of low quality. Migalastat showed varied effects on enzyme activity and substrate levels, with gender-specific differences noted in GL-3 substrate activity and eGFR. Overall, it improved cardiac and renal outcomes similarly to enzyme replacement therapy, with a comparable safety profile.

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)0.007
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
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 head0.051
GPT teacher head0.352
Teacher spread0.301 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2024
Admission routes1
Has abstractyes

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