Hospital readmission after anterior cruciate ligament reconstruction: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Anterior cruciate ligament (ACL) injury is one of the most common injuries of the knee. ACL reconstruction (ACLR) has been widely performed as a safe and effective treatment for ACL injuries. As there is an increasing trend in the incidence of ACL injury, hospital readmission after ACLR has attracted renewed attention for the financial burden to both patients and the healthcare system. However, information about hospital readmission after ACLR remains fragmented. Therefore, we plan to systematically review the literature to investigate the rate of, causes and risk factors for hospital readmission after ACLR, and summarise interventions to reduce hospital readmission. This article is to provide the protocol for an upcoming systematic review and meta-analysis on this important issue. Methods and analysis Reporting of this protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) checklist. Electronic databases, including PubMed, Embase and the Cochrane Library, will be systematically searched from inception to June 2020. No language restrictions will be applied. Studies will be included if they reported hospital readmission or explored the associated potential causes and risk factors for hospital readmission after ACLR. The primary outcome will be the number and time frame of hospital readmission after ACLR. Secondary outcomes will be reasons for readmission, number and types of complications, risk factors for readmission and preventive measures for readmission after ACLR. Quality assessments will be performed by using the Newcastle-Ottawa Scale (NOS). If possible, study results will be summarised in a forest plot, and heterogeneity will be tested by using the Cochran’s Q and I 2 statistics. Ethics and dissemination No ethical approval is required because our study is not related to patients or animals. The results will be published in a peer-reviewed journal. PROSPERO registration number CRD42020058624.
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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 | 0.084 | 0.150 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.023 | 0.032 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.005 |
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".