Prognostic Factors to Determine Survivorship of Meniscal Allograft Transplant: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: There is much room for improvement and optimization of meniscal allograft survivorship. PURPOSE: To understand prognostic factors for survivorship using evidence-based selection criteria in order to identify patients who would best benefit from meniscal allograft transplant (MAT). STUDY DESIGN: Systematic review; Level of evidence, 4. METHODS: We conducted this systematic review to analyze prognostic factors for survivorship of MAT. The Cochrane Central Register, PubMed publisher, Embase.com, and Web of Science databases were searched through August 8, 2019. Included studies entailed patients of any age who received MAT with a reported association between prognostic factors and survivorship of the allograft. Two reviewers independently screened all titles and abstracts for eligibility, extracted the data, assessed the risk of bias using the Newcastle-Ottawa Scale, and performed a best-evidence synthesis. RESULTS: The review included 18 studies with a total of 1920 patients. The mean follow-up time was 6.0 years (range, 2.1-11.2 years). A total of 20 prognostic factors were identified and shown to be associated with survivorship of MAT. Strong evidence was found that severe cartilage damage was associated with poor survivorship. Strong evidence was also found showing that sex, knee compartment, surgical side, concomitant anterior cruciate ligament (ACL) reconstruction, and concomitant osteotomy for malalignment had no effect on survivorship. Moderate evidence was found that body mass index (<36), tobacco use, and arthroscopic versus open procedure had no influence on survivorship. Conflicting evidence was found that older age and kissing cartilage lesions (lesions on both the femur and tibia vs on a single side) decreased survivorship. CONCLUSION: Severe cartilage damage decreases the survivorship of MAT. Concomitant ACL reconstruction and osteotomy showed no relationship to survivorship. Many determinants showed conflicting and limited evidence. Older age may be of interest and should be further studied.
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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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".