The quality of systematic reviews and other synthesis in the time of COVID-19
Bibliographic record
Abstract
COVID-19 research has been produced at an unprecedented rate and managing what is currently known is in part being accomplished through synthesis research. Here we evaluated how the need to rapidly produce syntheses has impacted the quality of the synthesis research. Thus, we sought to identify, evaluate and map the synthesis research on COVID-19 published up to 10 July 2020. A COVID-19 literature database was created using pre-specified COVID-19 search algorithms carried out in eight databases. We identified 863 citations considered to be synthesis research for evaluation in this project. Four-hundred and thirty-nine reviews were fully assessed with A MeaSurement Tool to Assess systematic Reviews (AMSTAR-2) and rated as very low-quality (n = 145), low-quality (n = 80), medium-quality (n = 208) and high-quality (n = 151). The quality of these reviews fell short of what is expected for synthesis research with key domains being left out of the typical methodology. The increase in risk of bias due to non-adherence to systematic review methodology is unknown and prevents the reader from assessing the validity of the review. The responsibility to assure the quality is held by both producers and publishers of synthesis research and our findings indicate there is a need to equip readers with the expertise to evaluate the review conduct before using it for decision-making purposes.
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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 | 0.709 | 0.915 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.012 |
| Bibliometrics | 0.057 | 0.066 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.050 | 0.029 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 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; 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".