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Record W4282832540 · doi:10.3233/jnd-210776

Comparing Deflazacort and Prednisone in Duchenne Muscular Dystrophy

2022· review· en· W4282832540 on OpenAlexaff
W. Douglas Biggar, Andrew J. Skalsky, Craig M. McDonald

Bibliographic record

VenueJournal of Neuromuscular Diseases · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsGeorgetown HospitalUniversity of Toronto
FundersPTC Therapeutics
KeywordsDeflazacortPrednisoneMedicineDuchenne muscular dystrophyPrednisoloneInternal medicineMuscular dystrophyPediatricsEndocrinology

Abstract

fetched live from OpenAlex

Deflazacort and prednisone/prednisolone are the current standard of care for patients with Duchenne muscular dystrophy (DMD) based on evidence that they improve muscle strength, improve timed motor function, delay loss of ambulation, improve pulmonary function, reduce the need for scoliosis surgery, delay onset of cardiomyopathy, and increase survival. Both have been used off-label for many years (choice dependent on patient preference, cost, and geographic location) before FDA approval of deflazacort for DMD in 2017. In this review, we compare deflazacort and prednisone/prednisolone in terms of their key pharmacological features, relative efficacy, and safety profiles in patients with DMD. Differentiating features include lipid solubility, pharmacokinetics, changes in gene expression profiles, affinity for the mineralocorticoid receptor, and impact on glucose metabolism. Evidence from randomized clinical trials, prospective studies, meta-analyses, and post-hoc analyses suggests that patients receiving deflazacort experience similar or slower rates of functional decline compared with those receiving prednisone/prednisolone. Regarding side effects, weight gain and behavior side effects appear to be greater with prednisone/prednisolone than with deflazacort, whereas bone health, growth parameters, and cataracts appear worse with deflazacort.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.036
GPT teacher head0.294
Teacher spread0.258 · 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.

Study designNot applicable
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

Citations54
Published2022
Admission routes1
Has abstractyes

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