It's Good to Feel Better But It's Better To Feel Good and Even Better to Feel Good as Soon as Possible for as Long as Possible. Response Criteria and the Importance of Change at OMERACT 10: Figure 1.
Bibliographic record
Abstract
The OMERACT patient reported outcomes (PRO) working group evaluated the methodologies for measuring responsiveness to change at the Outcome Measures in Rheumatology (OMERACT) 10 meeting. The outcome measures used in PRO studies are often expressed as continuous data at the group level (e.g., mean change in pain on a 0-100 visual analog scale). This is difficult to interpret and cannot easily be translated to the individual level of response. When interpreting scores at the individual level, it is important to take into account the following 4 main concepts: (1) improvement; (2) status of well-being; (3) onset of action; and (4) sustainability. Information from clinical trials on how many patients showed a response, what the level of response was, and how many patients are doing well, would be extremely useful for physicians. The objective of this article is to outline how continuous data may be reported in a clinically relevant manner. We will describe 5 techniques of reporting continuous variables in clinical studies and discuss the relevance of each.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 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".