Examining the Minimal Important Difference of Patient-reported Outcome Measures for Individuals with Knee Osteoarthritis: A Model Using the Knee Injury and Osteoarthritis Outcome Score
Bibliographic record
Abstract
OBJECTIVE: To examine the influence of different analytical methods, baseline covariates, followup periods, and anchor questions when establishing a minimal important difference (MID) for individuals with knee osteoarthritis (OA). Second, to propose MID for improving and worsening on the Knee injury and Osteoarthritis Outcome Score (KOOS). METHODS: Retrospective analysis of prospectively collected data from 272 patients with knee OA undergoing a multidisciplinary nonsurgical management strategy. The magnitude and rate of change as well as the influence of baseline covariates were examined for 5 KOOS subscales over 52 weeks. The MID for improving and worsening were investigated using 4 anchor-based methods. RESULTS: Waitlisted for joint replacement and exhibiting unilateral/bilateral symptoms influenced change in KOOS over time. Generally, low correlations between anchors and KOOS change scores limited calculations of MID; thus, they were only proposed for the pain, activities of daily living, and quality of life subscales. The method used to calculate the MID influenced the cutpoint; however, the type of anchor question only influenced the MID when analyzed with a particular mean change method. Depending on patient and clinical characteristics, the subscale, and the analytical approach used, the MID for KOOS improvement ranged from an absolute change of -1.5 to 20.6 points and worsening ranged from -19.17 to 8.5 points. CONCLUSION: MID vary with patient and clinical characteristics, KOOS subscale, and analytical approach. Provided the anchor question is relevant to the patient-reported outcome and baseline status is considered, the anchor does not appear to influence the MID for improvement or worsening when using some anchor-based methods.
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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 | 0.164 | 0.161 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".