It is good to feel better, but better to feel good: whether a patient finds treatment ‘successful’ or not depends on the questions researchers ask
Bibliographic record
Abstract
Introduction In sports physiotherapy, medicine and orthopaedic randomised controlled trials (RCT), the investigators (and readers) focus on the difference between groups in change scores from baseline to follow-up. Mean score changes are difficult to interpret (‘is an improvement of 20 units good?’), and follow-up scores may be more meaningful. We investigated how applying three different responder criteria to change and follow-up scores would affect the ‘outcome’ of RCTs. Responder criteria refers to participants’ perceptions of how the intervention affected them. Methods We applied three different criteria—minimal important change (MIC), patient acceptable symptom state (PASS) and treatment failure (TF)—to the aggregate Knee injury and Osteoarthritis Outcome Score (KOOS4) and the five KOOS subscales, the primary and secondary outcomes of the KANON trial ( ISRCTN84752559 ). This trial included young active adults with an acute ACL injury and compared two treatment strategies: exercise therapy plus early reconstructive surgery, and exercise therapy plus delayed reconstructive surgery, if needed. Results MIC: At 2 years, more than 90% in the two treatment arms reported themselves to be minimally but importantly improved for the primary outcome KOOS4. PASS: About 50% of participants in both treatment arms reported their KOOS4 follow-up scores to be satisfactory. TF: Almost 10% of participants in both treatment arms found their outcomes so unsatisfactory that they thought their treatment had failed. There were no statistically significant or meaningful differences between treatment arms using these criteria. Conclusion We applied change criteria as well as cross-sectional follow-up criteria to interpret trial outcomes with more clinical focus. We suggest researchers apply MIC, PASS and TF thresholds to enhance interpretation of KOOS and other patient-reported scores. The findings from this study can improve shared decision-making processes for people with an acute ACL injury.
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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 | 0.104 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".