Several factors predict the achievement of the patient acceptable symptom state and minimal clinically important difference for patient‐reported outcome measures following anterior cruciate ligament reconstruction: A systematic review
Bibliographic record
Abstract
PURPOSE: To summarize the predictors of the patient acceptable symptom state (PASS), minimal clinically important difference (MCID) and minimal important change (MIC) for patient-reported outcome measures (PROMs) following anterior cruciate ligament reconstruction (ACLR). METHODS: MEDLINE, PubMed and Embase were searched from inception to 5 January 2024. The authors adhered to PRISMA/R-AMSTAR guidelines, and the Cochrane Handbook for Systematic Reviews of Interventions. Data on statistical associations between predictive factors and PROMs were extracted. Inverse odds ratios (ORs) and confidence intervals (reverse group comparison) were calculated when appropriate to ensure comparative consistency. RESULTS: Thirteen studies comprising 21,235 patients (48.1% female) were included (mean age 29.3 years). Eight studies comprising 3857 patients identified predictors of PASS, including lateral extra-articular tenodesis (LET) (OR = 11.08, p = 0.01), hamstring tendon (HT) autografts (OR range: 2.02-2.63, p ≤ 0.011), age over 30 (OR range: 1.37-2.28, p ≤ 0.02), male sex (OR range: 1.03-1.32, p ≤ 0.01) and higher pre-operative PROMs (OR range: 1.04-1.21). Eight studies comprising 18,069 patients identified negative predictors of MCID or MIC, including female sex (OR = 0.93, p = 0.034), absence of HT autografts (OR = 0.70, p < 0.0001), higher pre-operative PROMs (OR = 0.76-0.84, p ≤ 0.01), meniscectomy (OR = 0.67, p = 0.014) and collision sports (OR = 0.02-0.60, p ≤ 0.05). CONCLUSION: Higher pre-operative PROMs, age over 30, male sex, LETs and HT autografts predicted PASS achievement. Lower pre-operative PROMs, male sex, non-collision sports, and lack of meniscectomies predicted MCID/MIC achievement. This review provides a comprehensive understanding of the predictors of clinically significant post-ACLR outcomes, thus improving clinical decision-making and the management of patient expectations. 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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".