New meniscal tears after ACL injury: what is the risk? A systematic review protocol
Bibliographic record
Abstract
Background Secondary meniscal tears after ACL injuries increase the risk of knee osteoarthritis. The current literature on secondary meniscal injuries after ACL injury is not consistent and may have methodological shortcomings. This protocol describes the methods of a systematic review investigating the rate of secondary meniscal injuries in children and adults after treatment (operative or non-operative) for ACL injury. Methods We will search electronic databases (Embase, Ovid Medline, Cochrane, CINAHL (Cumulative Index to Nursing and Allied Health Literature), SPORTDiscus, PEDro and Google Scholar) from database inception. Extracted data will include demographic data, methodology, intervention details and patient outcomes. Risk of bias will be assessed using the Newcastle Ottawa checklist for cohort studies. Article screening, eligibility assessment, risk of bias assessment and data extraction will be performed in duplicate by independent reviewers. A proportion meta-analysis will be performed if studies are homogeneous (I2<75%). If meta-analysis is precluded, data will be synthesised descriptively using best-evidence synthesis. The strength of recommendations and quality of evidence will be assessed using the Grading of Recommendations Assessment Development and Evaluation working group methodology. Ethics and dissemination This protocol is written according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses, and was registered in the International Prospective Register of Systematic Reviews on 22 March 2016. Trial registration number CRD42016036788.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.100 | 0.115 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.018 | 0.018 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.089 | 0.013 |
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".