Evaluating the quality of systematic reviews of comparative studies in autograft‐based anterior cruciate ligament reconstruction using the AMSTAR‐2 tool: A systematic umbrella review
Bibliographic record
Abstract
PURPOSE: There remains a lack of consensus around autograft selection in anterior cruciate ligament reconstruction (ACLR), though there is a large body of overlapping systematic reviews and meta-analyses. Systematic reviews and their methodological quality were aimed to be further assessed, using a validated tool known as assessing the methodological quality of systematic reviews (AMSTAR-2). METHODS: MEDLINE, Embase and CENTRAL were searched from inception to 23 April 2023 for systematic reviews (with/without meta-analysis) comparing primary ACLR autografts. A final quality rating from AMSTAR-2 was provided for each study ('critically low', 'low', 'moderate' or 'high' quality). Correlational analyses were conducted for ratings in relation to study characteristics. RESULTS: Two thousand five hundred and ninety-eight studies were screened, and 50 studies were ultimately included. Twenty-four studies (48%) were rated as 'critically low', 17 (34%) as 'low', seven (14%) as 'moderate' and two (4%) as 'high' quality. The least followed domains were reporting on sources of funding (1/50 studies), the impact of risk of bias on results of meta-analyses (11/36 studies) and publication bias (17/36 studies). There was a significant increase in the frequency of studies graded as 'moderate' compared to 'low' or 'critically low' quality over time (p = 0.020). CONCLUSION: The methodological quality of systematic reviews comparing autografts in ACLR is low, with many studies being rated lower due to commonly absent aspects of systematic review methodology such as investigating sources of funding and publication bias. More recent studies were generally more likely to be of higher quality. Authors are advised to consult AMSTAR-2 prior to conducting systematic reviews in ACLR. LEVEL OF EVIDENCE: Level IV.
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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 | 0.211 | 0.505 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.020 | 0.020 |
| Bibliometrics | 0.039 | 0.027 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".