Interpreting clinically significant changes in patient‐reported outcomes
Bibliographic record
Abstract
BACKGROUND: The goal of this study was to determine what magnitude of change in a patient-reported outcome score is clinically meaningful, so a clinicians' guide may be provided for estimating the minimal important difference (MID) when empiric estimates are not available. METHODS: Consecutive laryngeal cancer patients (n = 98) rated their quality of life (QOL) relative to other patients. These comparisons were contrasted with arithmetic differences in scores on the Functional Assessment of Cancer Therapy-Head and Neck (FACT-H&N) scale, Functional Assessment of Cancer Therapy-General (FACT-G) scale, 2 utility measures (the time tradeoff [TTO] and Daily Active Time Exchange [DATE]), and performance status (Karnofsky) scores. RESULTS: The FACT-H&N score needed to differ by 4% for average patients to rate themselves as "a little bit better" relative to other patients (95% CI, 1%-8%) and by 9% to rate themselves as "a little bit worse" relative to others (95% CI, 4%-13%). The corresponding values for other measures were FACT-G 4% (1%-7%) and 8% (95% CI, 5%-11%); TTO 5% (95% CI, 0%-11%) and 6% (95% CI, 0%-10%); DATE 5% (95%CI, 2%-9%) and 14% (95% CI, 0%-5%); Karnofsky 4% (95% CI, 1%-6%) and 10% (95% CI, 7%-13%). In each case, the minimal important difference (MID) was about 5% to 10% of the instrument range. CONCLUSIONS. One rule of thumb for interpreting a difference in QOL scores is a benchmark of about 10% of the instrument range. Patients appear to be more sensitive to favorable differences, so an improvement of 5% may be meaningful. This simple benchmark may be useful as a rough guide to meaningful 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 | 0.055 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".