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Record W2947808138 · doi:10.1186/s13023-019-1074-9

Recommendations for the management of MPS IVA: systematic evidence- and consensus-based guidance

2019· article· en· W2947808138 on OpenAlexafffund
Mehmet Umut Akyol, Tord D. Alden, Hernán Amartino, Jane Ashworth, Kumar G. Belani, Kenneth I. Berger, Andrea Borgo, Elizabeth Braunlin, Yoshikatsu Eto, Jeffrey I. Gold, Andrea Jester, Simon Jones, Cengiz Karsli, William G. Mackenzie, Diane Ruschel Marinho, Andrew McFadyen, Jim McGill, John J. Mitchell, Joseph Muenzer, Torayuki Okuyama, Paul J. Orchard, Bob Stevens, Sophie Thomas, Robert Walker, Robert Wynn, Roberto Giugliani, Paul Harmatz, Christian J. Hendriksz, Maurizio Scarpa

Bibliographic record

VenueOrphanet Journal of Rare Diseases · 2019
Typearticle
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsMontreal Children's HospitalHospital for Sick Children
FundersUniversidade Federal do Rio Grande do SulHospital de Clínicas de Porto AlegreUniversity of PretoriaActelion PharmaceuticalsSwedish Orphan BiovitrumJCR PharmaceuticalsAmicus TherapeuticsSanofi Genzymebluebird bioNational MPS SocietySanofiPTC TherapeuticsUltragenyx PharmaceuticalBioMarin PharmaceuticalHorizon PharmaMcGill UniversityAudentes TherapeuticsAlexion PharmaceuticalsPfizer
KeywordsHuman geneticsMEDLINEMedicinePolitical scienceBiologyGeneticsLaw

Abstract

fetched live from OpenAlex

INTRODUCTION: Mucopolysaccharidosis (MPS) IVA or Morquio A syndrome is an autosomal recessive lysosomal storage disorder (LSD) caused by deficiency of the N-acetylgalactosamine-6-sulfatase (GALNS) enzyme, which impairs lysosomal degradation of keratan sulphate and chondroitin-6-sulphate. The multiple clinical manifestations of MPS IVA present numerous challenges for management and necessitate the need for individualised treatment. Although treatment guidelines are available, the methodology used to develop this guidance has come under increased scrutiny. This programme was conducted to provide evidence-based, expert-agreed recommendations to optimise management of MPS IVA. METHODS: Twenty six international healthcare professionals across multiple disciplines, with expertise in managing MPS IVA, and three patient advocates formed the Steering Committee (SC) and contributed to the development of this guidance. Representatives from six Patient Advocacy Groups (PAGs) were interviewed to gain insights on patient perspectives. A modified-Delphi methodology was used to demonstrate consensus among a wider group of healthcare professionals with experience managing patients with MPS IVA and the manuscript was evaluated against the validated Appraisal of Guidelines for Research and Evaluation (AGREE II) instrument by three independent reviewers. RESULTS: A total of 87 guidance statements were developed covering five domains: (1) general management principles; (2) recommended routine monitoring and assessments; (3) disease-modifying interventions (enzyme replacement therapy [ERT] and haematopoietic stem cell transplantation [HSCT]); (4) interventions to support respiratory and sleep disorders; (5) anaesthetics and surgical interventions (including spinal, limb, ophthalmic, cardio-thoracic and ear-nose-throat [ENT] surgeries). Consensus was reached on all statements after two rounds of voting. The overall guideline AGREE II assessment score obtained for the development of the guidance was 5.3/7 (where 1 represents the lowest quality and 7 represents the highest quality of guidance). CONCLUSION: This manuscript provides evidence- and consensus-based recommendations for the management of patients with MPS IVA and is for use by healthcare professionals that manage the holistic care of patients with the intention to improve clinical- and patient-reported outcomes and enhance patient quality of life. It is recognised that the guidance provided represents a point in time and further research is required to address current knowledge and evidence gaps.

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)0.227
metaresearch head score (Gemma)0.443
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.227
Threshold uncertainty score0.954

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2270.443
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0080.011
Bibliometrics0.0220.014
Science and technology studies0.0040.005
Scholarly communication0.0110.011
Open science0.0110.013
Research integrity0.0150.012
Insufficient payload (model declined to judge)0.0070.003

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 head0.049
GPT teacher head0.340
Teacher spread0.291 · 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.

Study designSystematic review
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

Citations114
Published2019
Admission routes2
Has abstractyes

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