Quality assessment tools used in systematic reviews of in vitro studies: A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Systematic reviews (SRs) and meta-analyses (MAs) are commonly conducted to evaluate and summarize medical literature. This is especially useful in assessing in vitro studies for consistency. Our study aims to systematically review all available quality assessment (QA) tools employed on in vitro SRs/MAs. METHOD: A search on four databases, including PubMed, Scopus, Virtual Health Library and Web of Science, was conducted from 2006 to 2020. The available SRs/MAs of in vitro studies were evaluated. DARE tool was applied to assess the risk of bias of included articles. Our protocol was developed and uploaded to ResearchGate in June 2016. RESULTS: Our findings reported an increasing trend in publication of in vitro SRs/MAs from 2007 to 2020. Among the 244 included SRs/MAs, 126 articles (51.6%) had conducted the QA procedure. Overall, 51 QA tools were identified; 26 of them (51%) were developed by the authors specifically, whereas 25 (49%) were pre-constructed tools. SRs/MAs in dentistry frequently had their own QA tool developed by the authors, while SRs/MAs in other topics applied various QA tools. Many pre-structured tools in these in vitro SRs/MAs were modified from QA tools of in vivo or clinical trials, therefore, they had various criteria. CONCLUSION: Many different QA tools currently exist in the literature; however, none cover all critical aspects of in vitro SRs/MAs. There is a need for a comprehensive guideline to ensure the quality of SR/MA due to their precise nature.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.961 | 0.988 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.167 | 0.014 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.011 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it