Large variability in minimal clinically important difference, substantial clinical benefit and patient acceptable symptom state values among literature investigating patellar stabilization surgery: A systematic review
Bibliographic record
Abstract
PURPOSE: To investigate minimal clinically important difference (MCID), substantial clinical benefit (SCB), patient acceptable symptom state (PASS) values for patient-reported outcome measures (PROMs) after patellar stabilization surgery for patellar instability. Secondary outcomes included to describe methods to calculate clinically significant outcomes (CSOs), and to report on the achievement of these metrics. METHODS: On 31 July 2024, three databases were searched. Information on whether studies calculated MCID, SCB or PASS values or used previously established values was recorded. Data on study characteristics, CSO values, and the method of MCID quantification (e.g., distribution vs. anchor) were extracted. RESULTS: A total of 17 articles with 1447 patients (1462 knees) were included. A total of 18 unique outcome measures were reported. Six out of 15 (40%), 2 out of 5 (40%), and zero studies used prior established values for MCID, SCB and PASS, respectively. MCID ranged widely (e.g., International Knee Documentation Committee [IKDC]: 5.6-20.5; Kujala Anterior Knee Pain Scale: 5.38-11.9 and Lysholm: 5.6-11.1). Fourteen out of 15 utilized a distribution-based method to calculate MCID, with only one study using an anchor-based method. SCB values ranged widely as well (e.g. , IKDC: 14.5-23.6; Knee Osteoarthritis and Outcome Score [KOOS] symptoms: 4.2-14.2 and KOOS activities of daily living [ADLs]: 6.5-25.7). Large variability was found among percentages of patients that achieved MCID values (e.g. , IKDC: 28%-98.6%, Kujala: 38%-100%, Lysholm: 44%-98.4% and Tegner: 84%-95%). CONCLUSION: The significant heterogeneity in reported thresholds for MCID, SCB and PASS across studies highlights critical challenges in interpreting results after patellar stabilization surgery, specifically regarding what constitutes a clinically relevant outcome. MCID was the most commonly reported metric and calculated predominantly with distribution-based methods, with over half of the studies using previously established thresholds. PASS and SCB were widely underreported as well, suggesting a need for studies investigating patellar stabilization to prioritize the calculation of all three metrics, using anchor-based techniques. LEVEL OF EVIDENCE: Level IV.
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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 | 0.024 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".