Developing an OMERACT Core Outcome Set for Assessing Safety Components in Rheumatology Trials: The OMERACT Safety Working Group
Bibliographic record
Abstract
OBJECTIVE: Failure to report harmful outcomes in clinical research can introduce bias favoring a potentially harmful intervention. While core outcome sets (COS) are available for benefits in randomized controlled trials in many rheumatic conditions, less attention has been paid to safety in such COS. The Outcome Measures in Rheumatology (OMERACT) Filter 2.0 emphasizes the importance of measuring harms. The Safety Working Group was reestablished at the OMERACT 2016 with the objective to develop a COS for assessing safety components in trials across rheumatologic conditions. METHODS: The safety issue has previously been discussed at OMERACT, but without a consistent approach to ensure harms were included in COS. Our methods include (1) identifying harmful outcomes in trials of interventions studied in patients with rheumatic diseases by a systematic literature review, (2) identifying components of safety that should be measured in such trials by use of a patient-driven approach including qualitative data collection and statistical organization of data, and (3) developing a COS through consensus processes including everyone involved. RESULTS: Members of OMERACT including patients, clinicians, researchers, methodologists, and industry representatives reached consensus on the need to continue the efforts on developing a COS for safety in rheumatology trials. There was a general agreement about the need to identify safety-related outcomes that are meaningful to patients, framed in terms that patients consider relevant so that they will be able to make informed decisions. CONCLUSION: The OMERACT Safety Working Group will advance the work previously done within OMERACT using a new patient-driven approach.
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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 | 0.719 | 0.785 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.008 | 0.029 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".