Improving Benefit-harm Assessment of Therapies from the Patient Perspective: OMERACT Premeeting Toward Consensus on Core Sets for Randomized Controlled Trials
Bibliographic record
Abstract
OBJECTIVE: Outcome Measures in Rheumatology (OMERACT) convened a premeeting in 2018 to bring together patients, regulators, researchers, clinicians, and consumers to build upon previous OMERACT drug safety work, with patients fully engaged throughout all phases. METHODS: Day 1 included a brief introduction to the history of OMERACT and methodology, and an overview of current efforts within and outside OMERACT to identify patient-reported medication safety concerns. On Day 2, two working groups presented results; after each, breakout groups were assembled to discuss findings. RESULTS: Five themes pertaining to drug safety measurement emerged. CONCLUSION: Current approaches have failed to include data from the patient's perspective. A better understanding of how individuals with rheumatic diseases view potential benefits and harms of therapies is essential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.855 | 0.838 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.013 | 0.034 |
| Research integrity | 0.021 | 0.047 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".