Deflazacort for the treatment of Duchenne Dystrophy: A systematic review
Bibliographic record
Abstract
BACKGROUND: To complete a systematic review and meta-analysis based on the clinical question: Is Deflazacort (DFZ), a prednisolone derivative, an effective therapy for improving strength, with acceptable side effects, in children with Duchenne Dystrophy (DD)? METHODS: MEDLINE, EMBASE, Current Contents, Dissertation Abstracts, Health Star, PsychINFO and Cochrane, were searched using the following inclusion criteria: 1) A randomized controlled trial comparing DFZ with placebo or another therapy; 2) Male participants age 2-18 years with DD; 3) Outcomes of (a) any form of strength or functional testing, or (b) any form of side effect. RESULTS: Fifteen studies of potential relevance were identified, with five meeting the inclusion criteria. These five studies included 291 children and were published in English language journals between 1994 and 2000. Two studies compared DFZ versus placebo, two studies compared DFZ with prednisone and one study had both placebo and prednisone comparisions. Two large trials were identified that have not been published in article format. Due to the heterogeneity in outcome measures and the inconsistent reporting of summary statistics a meta-analytic approach could not be taken. CONCLUSIONS: Examining individual studies it appears that DFZ improves strength and functional outcomes compared to placebo, but it remains unclear if it has a benefit over prednisone on similar outcomes. Two trials found that DFZ causes less weight gain than prednisone.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.018 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".