‘Am I doing this right?’ Physician perceptions of the global assessment in clinical trials of systemic sclerosis
Bibliographic record
Abstract
OBJECTIVES: Physician global assessments (PhyGAs) are commonly performed in randomized controlled trials (RCTs) in SSc. However, there is no single PhyGA applied across RCTs. We performed an exploratory qualitative study to explore perceptions of the PhyGA, its role in RCTs and how physicians perform their own assessment. METHODS: Participants with expertise in the clinical assessment and, or actively involved in research on SSc were invited to participate. Participants were asked to define disease constructs of activity, damage, severity, and overall health, and to describe how they perform a PhyGA and their perception of what a PhyGA should assess. Interview transcripts were analysed using deductive and inductive thematic analysis. RESULTS: Eighteen rheumatologists and one patient research partner were interviewed. Four major themes were identified: (i) physician uncertainty; (ii) variation in the conduct of a PhyGA; (iii) physician efforts to improve PhyGA consistency; (iv) utility of a PhyGA. Most participants felt a PhyGA should assess changeable aspects of SSc, commonly conceived of as disease activity. There was considerable uncertainty about the optimal method for assessing disease activity. Participants were uncertain about their own methods of performing a PhyGA, and variability in the application of the instrument was identified. Despite these limitations, physicians generally agreed that the PhyGA is useful and can assess unquantifiable aspects of SSc. CONCLUSION: We identified significant heterogeneity in the approach to PhyGAs in SSc. This variation was considered a limitation of the PhyGA. Overall, a PhyGA was viewed as a useful instrument that can aid the assessment of treatment response in RCTs.
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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 | 0.353 | 0.535 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".