Identifying approaches for assessing methodological and reporting quality of systematic reviews: a descriptive study
Bibliographic record
Abstract
BACKGROUND: The methodological quality and completeness of reporting of the systematic reviews (SRs) is fundamental to optimal implementation of evidence-based health care and the reduction of research waste. Methods exist to appraise SRs yet little is known about how they are used in SRs or where there are potential gaps in research best-practice guidance materials. The aims of this study are to identify reports assessing the methodological quality (MQ) and/or reporting quality (RQ) of a cohort of SRs and to assess their number, general characteristics, and approaches to 'quality' assessment over time. METHODS: The Cochrane Library, MEDLINE®, and EMBASE® were searched from January 1990 to October 16, 2014, for reports assessing MQ and/or RQ of SRs. Title, abstract, and full-text screening of all reports were conducted independently by two reviewers. Reports assessing the MQ and/or RQ of a cohort of ten or more SRs of interventions were included. All results are reported as frequencies and percentages of reports. RESULTS: Of 20,765 unique records retrieved, 1189 of them were reviewed for full-text review, of which 76 reports were included. Eight previously published approaches to assessing MQ or reporting guidelines used as proxy to assess RQ were used in 80% (61/76) of identified reports. These included two reporting guidelines (PRISMA and QUOROM) and five quality assessment tools (AMSTAR, R-AMSTAR, OQAQ, Mulrow, Sacks) and GRADE criteria. The remaining 24% (18/76) of reports developed their own criteria. PRISMA, OQAQ, and AMSTAR were the most commonly used published tools to assess MQ or RQ. In conjunction with other approaches, published tools were used in 29% (22/76) of reports, with 36% (8/22) assessing adherence to both PRISMA and AMSTAR criteria and 26% (6/22) using QUOROM and OQAQ. CONCLUSIONS: The methods used to assess quality of SRs are diverse, and none has become universally accepted. The most commonly used quality assessment tools are AMSTAR, OQAQ, and PRISMA. As new tools and guidelines are developed to improve both the MQ and RQ of SRs, authors of methodological studies are encouraged to put thoughtful consideration into the use of appropriate tools to assess quality and reporting.
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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 | 0.419 | 0.680 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.060 | 0.058 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".