Endpoints: consensus recommendations from OMERACT IV
Bibliographic record
Abstract
The goal of the 'Outcome Measures in Rheumatology' (OMERACT) process is to select domains and/or outcome measures for clinical trials in each defined disease category according to truth, discrimination and feasibility. OMERACT IV, held in Cancun, Mexico, April 1998, included the module 'Systemic Lupus Erythematosus (SLE)', designed to define a preliminary core set of outcome domains for randomized controlled trials and longitudinal observational studies (LOS). Although specific measures to be used in clinical trials of SLE have yet to be determined, both randomized controlled trials and longitudinal observation studies groups recommended that outcome be assessed in terms of disease activity and damage in all organ systems involved, as well as by health related quality of life, meaningful to patients, and adverse events. These recommendations were ratified by the majority of participants. In a heterogeneous patient population such as SLE, it is recognized that any individual measure of clinical response may reflect only a portion of what might be termed the 'true outcome'. A responder index could integrate such relatively independent measures of outcome into a single assessment, potentially increasing statistical power and decreasing sample size. Results from randomized controlled trials currently underway assessing these outcome domains are eagerly awaited, and are expected to rapidly advance the field.
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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 | 0.068 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.013 | 0.006 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.021 |
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".