Assessing the Amount of Change in an Outcome Measure Is Not the Same as Assessing the Importance of Change
Bibliographic record
Abstract
PURPOSE: To determine whether a difference exists between patients' self-ratings of amount of change and their self-ratings of importance of change. METHODS: Eighty-eight patients receiving treatment of low-back pain completed two global rating of change (GRC) scales 4 to 6 weeks after their initial assessments. The scales were similar in format, differing only in that one asked respondents about the amount of change and the other about the importance of change. RESULTS: Our analysis was restricted to 86 patients who reported improvement or no change. The chance-corrected agreement between patients' self-ratings of amount of change and their self-ratings of importance of change was low (κ=0.35; 95% CI, 0.23-0.48). Of 47 disagreements, 44 reported a greater importance of change than amount of change and 3 reported a greater amount of change than importance of change. CONCLUSIONS: Assessing the amount of change is not the same as assessing the importance of change. When the goal is to estimate important change, the reference standard should direct patients to judge the importance of the change.
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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 | 0.051 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".