Patient Perspective Workshop: Moving Towards OMERACT Guidelines for Choosing or Developing Instruments to Measure Patient-Reported Outcomes
Bibliographic record
Abstract
The workshop Choosing or Developing Instruments held at the Outcome Measures in Rheumatology (OMERACT) 10 meeting was designed to help participants think about the underlying methods of instrument development. Conference pre-reading material and 3 brief introductory presentations elaborated the issues, and participants broke into discussion groups before reconvening to share insights, engage in a more general discussion of the issues, and vote on recommendations. Tradeoffs between using current imperfect measures and the long and complex process of developing new instruments were considered, together with the need for rigor in patient-reported outcome (PRO) instrument development. The main considerations for PRO instrument development were listed and a research agenda for action produced. As part of the agenda for action, it is recommended that researchers and patient partners work together to tackle these issues, and that OMERACT bring forward proposals for acceptable instrument development protocols that would meet an enhanced "Truth" statement in the OMERACT Filter.
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 | 0.301 | 0.276 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.014 | 0.027 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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".