Development of a New Self-Reporting Instrument Measuring Benefits and Side Effects of Corticosteroids in Duchenne Muscular Dystrophy: Report from a Pilot Study
Bibliographic record
Abstract
BACKGROUND: There is no cure for Duchenne Muscular Dystrophy (DMD); treatment is symptomatic and corticosteroids slow the progression. Side effects of corticosteroids - especially the physical effects - have been described, however patients' and caregivers perception on chronic corticosteroid treatment and their side effects is less well known, in particular with regards to cognition, behaviour, and emotional functioning. OBJECTIVE: The primary aim of this pilot study was to (i) construct a self-report questionnaire to assess the perceived benefits and side effects of corticosteroids for patients with DMD and their parents. Furthermore we aimed to (ii) investigate the psychometric qualities of this questionnaire, (iii) whether there was a difference between parents' and patient's perceptions, and finally (iv) to what extent reported side effects may alter over time. METHODS: A 23-item questionnaire (SIDECORT: side effect of corticosteroids) was constructed to assess the perception of these benefits and side effects in a systematic manner. RESULTS: In total, 86 patients (aged 5 - 28 years) and 125 of their parents completed the questionnaire. Internal consistency was good. Using factor analyses on the side effect items as reported by parents, two underlying factors were found, with the first factor describing cognitive, behavioural and emotional functioning, and the second factor describing physical functioning. The potential benefits of corticosteroids were highly rated among both parents and patients, although parents rated the importance of the benefits higher than their sons (p = 0.002). Similarly, parents rated the severity of the side effects generally higher than their sons (p = 0.011), especially with regards to the physical side effects (p = 0.014). Based on the parent's perception, the neurodevelopmental side effects generally appeared to decline the longer corticosteroids were used. CONCLUSIONS: To our knowledge, this is the first explicit study on perceived cognitive-, behavioural-, and emotional side effects and the allocation of benefits to corticosteroids in DMD. On the basis of our research we suggest a short form questionnaire, which proves to be reliable and valid for research- and clinical practice. This questionnaire could provide useful insights for the care of boys and men with DMD.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".