Bibliographic record
Abstract
The Outcome Measures in Rheumatology (OMERACT) community strives to develop core outcome sets for rheumatologic conditions to specify, for each condition, the minimum set of outcomes necessary to provide consistent estimates of the benefits of an intervention. The original and successful OMERACT filter of "truth, discrimination, and feasibility" requires development and updating because of application to a widening range of conditions by an expanding group, particularly patients. It should more explicitly identify the relevant core outcomes that might be universal to all randomized controlled trials within rheumatology. Working from first principles, comparing proposals against actual procedures adopted by OMERACT working groups, and seeking a broad consensus over several major sessions at the OMERACT 11 meeting, a new version has emerged, OMERACT Filter 2.0, which will form the central theme of the intended OMERACT handbook and offers an approach to core outcome set development in many areas of healthcare.
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 | 0.311 | 0.493 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".