Interchangeability of patient pain, fatigue and global scores in patients with spondyloarthritis - a registry-based simulation study
Bibliographic record
Abstract
BACKGROUND: To investigate a patient-level single imputation approach for patient reported outcomes (PROs) that express similar contents or associated PROs, where a PRO whose value is missing at a particular timepoint is substituted by another PRO whose value is available at the same timepoint. METHODS: We performed a simulation study on registry-based spondyloarthritis data to explore the potential interchangeability between the patient pain (PPA) and fatigue (PFA) assessment scores and relevant Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) individual questions, and between PPA, PFA and patient global assessment (PGA). Performance was assessed per imputation method in terms of relative bias and coverage. Sample size, level of missingness and missing data pattern were included as parameters in the simulations. RESULTS: All applied scenarios to interchange PPA with BASDAI question 2 (axial pain), BASDAI question 3 (peripheral joint pain/swelling) or their average failed. Interchangeability between PFA and BASDAI question 1 (fatigue/tiredness) was acceptable for partially (up to 50%) missing data. When interchanging patient assessment scores (PPA, PFA and PGA), we observed inconsistent results in terms of performance. The performance of the applied methods depended on the sample size and the level of missingness, but not heavily on the underlying missing data pattern. CONCLUSIONS: Interchanging PFA and the BASDAI fatigue question was justified for partially missing data, while interchangeability between PPA, PFA and PGA, and between PPA and the BASDAI pain questions was not advised. Our findings suggest that registering patient assessment scores and BASDAI questions is recommended.
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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 | 0.066 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".