Comparison of Three Physician Global Assessment Instruments in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Physician global assessments (PhyGAs) are variably applied in systemic sclerosis (SSc) clinical trials. The comparability of different PhyGA results is unknown. We sought to assess the comparability of results from three different PhyGA instruments simultaneously applied in the Australian Scleroderma Cohort Study (ASCS). METHODS: Using data from 1,965 ASCS participants, we assessed the correlation between results of three PhyGA assessments: (1) overall health, (2) activity, and (3) damage. We evaluated the concordance of change in each PhyGA between study visits. Ordered logistic regression analysis was used to evaluate the clinical associations of each PhyGA. RESULTS: The absolute scores of each PhyGA were strongly correlated at individual study visits. Concordant changes of the PhyGA scores occurred between 50% of study visits. Only patient-reported breathlessness was associated with all three PhyGA scores (overall health: odds ratio [OR] 1.67, P < 0.01; activity: OR 1.44, P < 0.01; damage: OR 1.32, P < 0.01). Changes in physician-assessed activity scores were also associated with patient-reported worsening skin disease (OR 1.25, P = 0.03) and fecal incontinence (OR 1.23, P = 0.01), whereas damage scores were associated with respiratory disease (pulmonary arterial hypertension: OR 1.25, P = 0.03; chronic obstructive pulmonary disease: OR 1.37, P = 0.04), as well as skin scores (OR 1.02, P < 0.01) and fecal incontinence (OR 1.21, P = 0.02). CONCLUSION: PhyGAs of overall health, activity, and damage are each associated with different SSc features, and changes in different PhyGA scores are discordant 50% of the time. Our findings suggest results of variably worded PhyGAs are not directly interchangeable and support the development of a standardized PhyGA.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".