Systematic Review of Patient Outcomes and Associated Predictors After Microfracture in the Patellofemoral Joint
Bibliographic record
Abstract
We summarized the clinical outcomes and predictors of clinical outcomes after microfracture for chondral lesions in the patellofemoral joint (PFJ). METHODS: Embase, PubMed, CENTRAL, BIOSIS, and CINAHL databases were searched between January 1, 1980, and January 1, 2019, to identify all articles that examined outcomes or predictors of outcomes of microfracture in patients with patellofemoral chondral lesions. Studies of full-thickness chondral lesions in the PFJ were included, whereas those involving adolescents, partial-thickness chondral lesions, and underlying patellar instability were excluded. RESULTS: We found a total of 257 articles, of which 8 articles (174 patients) met our inclusion criteria. All studies found improvement in clinical outcomes after microfracture in the PFJ. Younger patients showed greater improvement in clinical outcomes than older patients. However, the effect of size, severity (grade), or location of chondral lesions on clinical outcomes after microfracture is unclear. CONCLUSION: We found improvement in clinical outcomes after microfracture in the PFJ at midterm follow-up. Age may be a predictor of successful outcomes and longevity of the repair; however, there is insufficient evidence regarding the influence of defect size, severity, and location on clinical outcomes.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".