AI to Accelerate Meta-Analysis: Screening + Extraction

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-research-AI-meta-analysis-acceleration-creators

1. What is the primary bottleneck in traditional meta-analysis that AI assistance aims to address?

   1. Screening and data extraction tedium
   2. Statistical analysis complexity
   3. Manuscript writing time
   4. Literature database access

2. When using AI for title and abstract screening in a meta-analysis, what validation step is required before full implementation?

   1. Validating accuracy against a human-coded subset
   2. Running the AI on open-access publications only
   3. Obtaining institutional review board approval
   4. Training the AI on the entire dataset

3. Why should AI-assisted data extraction follow a pre-specified extraction template?

   1. To allow the AI to learn from unstructured data
   2. To ensure consistent extraction across studies and enable verification
   3. To meet journal submission requirements
   4. To speed up the extraction process regardless of quality

4. In a dual-reviewer methodology that includes AI, how should the two reviewers be structured?

   1. Human reviews all studies while AI operates independently without comparison
   2. AI serves as one reviewer and a human serves as the second reviewer
   3. AI reviews first, then a human reviews only the AI-rejected studies
   4. AI performs extraction while humans perform screening only

5. What is the purpose of following PRISMA-AI guidance when conducting an AI-assisted systematic review?

   1. To document the AI methodology transparently for reproducibility
   2. To meet funding agency requirements
   3. To ensure statistical software compatibility
   4. To validate the meta-analysis findings

6. Before beginning an AI-assisted meta-analysis study, where should the protocol be published?

   1. In a preprint repository
   2. On PROSPERO
   3. In a peer-reviewed journal
   4. On the researcher's personal website

7. A researcher wants to use AI to fully automate screening and skip human review entirely. Why is this problematic?

   1. The AI might not have internet access
   2. AI cannot read PDF files
   3. Courts of scientific opinion still expect human judgment
   4. Human review is slower than AI

8. Why should human reviewers NOT trust AI decisions on close-call studies for inclusion in a meta-analysis?

   1. AI models lack contextual judgment for ambiguous cases
   2. Human reviewers are always faster than AI
   3. Close-call studies are always irrelevant
   4. AI cannot handle citation formatting

9. What type of source papers will AI likely fail to extract accurate data from, even with a good template?

   1. Peer-reviewed publications
   2. Well-structured RCT reports
   3. Open-access journals
   4. Poorly-structured source papers

10. A student designs a workflow where AI screens all abstracts, extracts data from included studies, and a human only checks the final reference list. This violates which lesson principle?

    1. PRISMA-AI documentation
    2. PROSPERO registration
    3. Dual-reviewer methodology
    4. Template-based extraction

11. What distinguishes a systematic review from a traditional literature review, according to the workflow described?

    1. Systematic reviews are faster to complete
    2. Systematic reviews follow PRISMA guidelines and pre-specified protocols
    3. Systematic reviews examine fewer studies
    4. Systematic reviews always use AI

12. During AI-assisted data extraction, what does the verification protocol ensure?

    1. The AI is trained on more data
    2. Extracted data matches the template correctly and errors are caught
    3. The human reviewer agrees with every AI decision
    4. The study is published in a high-impact journal

13. A researcher trains their AI screening model on 80% of their target database and validates on the remaining 20%. What is problematic about this approach?

    1. AI cannot be trained for screening
    2. The validation set should be human-coded, not just held-out data
    3. The training set is too small
    4. The split should be 50/50

14. Why is quality assessment (like risk of bias tools) integrated into the AI-assisted meta-analysis workflow?

    1. To replace human reviewers
    2. To satisfy journal page limits
    3. To train the AI model faster
    4. To assess the included studies' methodological quality

15. If an AI screening tool achieves 95% accuracy on a validation subset, what should researchers do before using it for full-text screening?

    1. Discard the tool and screen manually
    2. Investigate the 5% errors to understand failure modes and determine if human oversight is needed for similar cases
    3. Publish the accuracy result without further review
    4. Use it immediately since accuracy is high