Comparative clinical outcomes of different therapies for traumatic meniscal tears in adults: A protocol for systematic review and network meta-analysis.
Bibliographic record
Abstract
BACKGROUND: Meniscus tears are usually classified as degenerative or traumatic tears according to their pathogenesis. At present, traumatic meniscal tears are generally believed to have high healing potential. In recent years, multiple treatments have been described for traumatic meniscal tears, such as the inside-out technique, outside-in technique, all-inside technique, biological augmentation of meniscal repair, meniscectomy, and non-surgical treatment. However, the functional recovery of the knee joint and healing of the meniscus after treatment are quite different from the results reported in the literature, which requires more reliable evidence-based medical findings. This study will evaluate evidence from multiple types of research comparing different therapies for traumatic meniscal tears in adults. METHODS: We will search the EMBASE, Cochrane Library (the Cochrane Database of Systematic Reviews, the Cochrane Central Register of Controlled Trials [CENTRAL], Cochrane Methodology Register), PubMed, Web of Science (Science and Social Science Citation Index), China Knowledge Network, CBM, Wanfang data, and VIP electronic databases from their inception to August 10, 2021, with no language restrictions. We will also manually search Baidu and Google Scholar to identify randomized controlled studies, non-randomized controlled studies, and cohort studies on the treatment of traumatic meniscal tears. Two researchers will independently screen the literature, extract the data, and evaluate the quality of the studies. Software programs, including Microsoft Access, Excel, Stata (Version 15), WinBUGS (Version 1.4.3), and ADDIS (Version 1.16.8), were used to analyze and manipulate the data. RESULTS: In this study, the main outcomes were physical function and healing rate, based on the Western Ontario and McMaster Universities Osteoarthritis Index, Lysholm Knee Scoring Scale, Knee Injury and Osteoarthritis Outcome Score, Functional Recovery Scale, and clinical healing rate. The secondary indexes included total cost, cost-effectiveness ratio, incremental cost-effectiveness ratio, Tegner activity scale score, visual analogue scale, numerical rating scale, and meniscal tear complications. CONCLUSIONS: This systematic review will provide reliable evidence-based findings for the clinical application of different therapies for traumatic meniscal tears in adults.
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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 | 0.063 | 0.093 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.003 |
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".