Source: https://github.com/aipoch/medical-research-skills
Systematic Review (Meta Skill)
This meta-skill coordinates a complete systematic review pipeline following
PRISMA 2020 guidelines. It integrates multi-database literature searching,
structured screening, information extraction, quantitative synthesis, and
standardized reporting into a rigorous evidence review workflow by combining
three specialized skills.
Workflow
Step 1: Protocol Development
Define the review protocol before conducting any searches:
- Formulate the research question using the PICO framework
(Population, Intervention, Comparator, Outcome)
- Establish inclusion and exclusion criteria with explicit justification
- Define the search strategy: databases, date range, language restrictions
- Specify outcome measures and effect size metrics
- Pre-register the protocol (PROSPERO or OSF recommended)
- Document any planned sensitivity or subgroup analyses
Step 2: Multi-Database Systematic Search
Execute comprehensive searches across multiple bibliographic databases:
- PubMed/MEDLINE: Biomedical and clinical literature via structured MeSH queries
- arXiv: Preprints in quantitative and computational fields
- Semantic Scholar: AI-augmented citation graph and full-text search
- CrossRef: DOI-based metadata and cross-publisher discovery
- Construct database-specific search strings from the master strategy
- Document exact queries, dates, result counts; deduplicate exports
- Supplement with citation chaining (forward and backward) on key papers
Step 3: Screening and Eligibility Assessment
Apply a two-stage screening process to identify eligible studies:
- Title/abstract screening: Apply inclusion criteria, flag uncertain cases
- Full-text assessment: Evaluate against all criteria, document exclusion reasons
- Track inter-rater agreement (Cohen's kappa) if multiple reviewers
- Maintain a log of all screening decisions for the PRISMA flow diagram
- Resolve disagreements through discussion or third-reviewer arbitration
Step 4: Structured Data Extraction
Extract pre-defined data elements from each included study:
- Study characteristics: design, setting, sample size, follow-up duration
- Population, intervention, comparator: demographics, dosage, duration
- Outcomes and results: endpoints, effect estimates, confidence intervals
- Quality indicators: randomization method, blinding, attrition, funding
Use ScienceClaw information extraction to assist with structured data capture
from PDF full texts, reducing manual effort and transcription errors.
Step 5: Risk of Bias and Quality Assessment
Evaluate methodological quality of each included study:
- Apply appropriate tools (RoB 2 for RCTs, ROBINS-I for non-randomized, Newcastle-Ottawa)
- Assess each domain: selection, performance, detection, attrition, reporting
- Generate risk-of-bias summary figures (traffic light plots)
- Evaluate overall certainty of evidence using GRADE framework
- Document judgments with supporting quotations from study texts
Step 6: Meta-Analysis and Evidence Synthesis
Perform quantitative synthesis when studies are sufficiently homogeneous:
- Calculate standardized effect sizes (SMD, OR, RR, HR as appropriate)
- Fit random-effects or fixed-effects meta-analysis models
- Generate forest plots with study-level and pooled estimates
- Assess heterogeneity: I-squared statistic, Cochran's Q test, tau-squared
- Subgroup and sensitivity analyses: leave-one-out, trim-and-fill, funnel plots
Step 7: PRISMA Reporting and Final Output
Compile the review following PRISMA 2020 reporting standards:
- PRISMA flow diagram with identification, screening, eligibility, inclusion counts
- Characteristics of included studies table
- Risk-of-bias summary and individual study assessments
- Forest plots, funnel plots, and subgroup analysis figures
- Summary of findings table with GRADE certainty ratings
- Complete PRISMA 2020 checklist (Page et al., BMJ 2021;372:n71) cross-referenced
to report sections
Integration Points
- literature-search -- Multi-database querying, deduplication, citation chaining, export
- scienceclaw-ie -- Structured data extraction from PDFs, entity recognition, table parsing
- paper-writing -- PRISMA-compliant report generation, figure formatting, reference management
Output Formats
- PRISMA flow diagram: Study counts at each screening stage with exclusion reasons
- Study characteristics table: Design, population, intervention, outcomes per study
- Forest plot: Effect sizes with CIs, weights, pooled estimate, heterogeneity stats
- Risk-of-bias table: Domain-level judgments per study with traffic light visualization
- Summary of findings: GRADE-rated evidence table for each outcome
- Full report: PRISMA 2020 compliant manuscript with all required sections
PRISMA 2020 Checklist Reference
This workflow aligns with the PRISMA 2020 statement (Page et al., BMJ
2021;372:n71). The 27-item checklist spans title through other information,
and each workflow step maps to specific checklist items to ensure completeness.
Best Practices
- Register the protocol before beginning searches to reduce reporting bias
- Use at least two independent reviewers for screening and extraction
- Document every decision point for full transparency and reproducibility
- Never modify inclusion criteria after seeing search results without justification
- Report all pre-planned analyses regardless of statistical significance
- Use GRADE to rate certainty of evidence for each outcome separately
- Clearly distinguish direct evidence from indirect comparisons
- Acknowledge limitations in study-level quality and review-level methodology
- Update the review when substantial new evidence becomes available
- Make extracted data and analysis code publicly available when possible
Input Validation
This skill accepts requests that match the documented purpose of systematic-review and include enough context to complete the workflow safely.
Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
systematic-review only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
1---2name: systematic-review3description: Orchestrates a systematic review and meta-analysis workflow following PRISMA 2020 guidelines, from protocol development through multi-database search, screening, data extraction, and evidence synthesis. Use when conducting evidence-based reviews, meta-analyses, or scoping revi...4license: MIT5---6> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)789# Systematic Review (Meta Skill)1011This meta-skill coordinates a complete systematic review pipeline following12PRISMA 2020 guidelines. It integrates multi-database literature searching,13structured screening, information extraction, quantitative synthesis, and14standardized reporting into a rigorous evidence review workflow by combining15three specialized skills.1617## Workflow1819### Step 1: Protocol Development2021Define the review protocol before conducting any searches:22- Formulate the research question using the PICO framework23 (Population, Intervention, Comparator, Outcome)24- Establish inclusion and exclusion criteria with explicit justification25- Define the search strategy: databases, date range, language restrictions26- Specify outcome measures and effect size metrics27- Pre-register the protocol (PROSPERO or OSF recommended)28- Document any planned sensitivity or subgroup analyses2930### Step 2: Multi-Database Systematic Search3132Execute comprehensive searches across multiple bibliographic databases:33- **PubMed/MEDLINE**: Biomedical and clinical literature via structured MeSH queries34- **arXiv**: Preprints in quantitative and computational fields35- **Semantic Scholar**: AI-augmented citation graph and full-text search36- **CrossRef**: DOI-based metadata and cross-publisher discovery37- Construct database-specific search strings from the master strategy38- Document exact queries, dates, result counts; deduplicate exports39- Supplement with citation chaining (forward and backward) on key papers4041### Step 3: Screening and Eligibility Assessment4243Apply a two-stage screening process to identify eligible studies:44- **Title/abstract screening**: Apply inclusion criteria, flag uncertain cases45- **Full-text assessment**: Evaluate against all criteria, document exclusion reasons46- Track inter-rater agreement (Cohen's kappa) if multiple reviewers47- Maintain a log of all screening decisions for the PRISMA flow diagram48- Resolve disagreements through discussion or third-reviewer arbitration4950### Step 4: Structured Data Extraction5152Extract pre-defined data elements from each included study:53- Study characteristics: design, setting, sample size, follow-up duration54- Population, intervention, comparator: demographics, dosage, duration55- Outcomes and results: endpoints, effect estimates, confidence intervals56- Quality indicators: randomization method, blinding, attrition, funding5758Use ScienceClaw information extraction to assist with structured data capture59from PDF full texts, reducing manual effort and transcription errors.6061### Step 5: Risk of Bias and Quality Assessment6263Evaluate methodological quality of each included study:64- Apply appropriate tools (RoB 2 for RCTs, ROBINS-I for non-randomized, Newcastle-Ottawa)65- Assess each domain: selection, performance, detection, attrition, reporting66- Generate risk-of-bias summary figures (traffic light plots)67- Evaluate overall certainty of evidence using GRADE framework68- Document judgments with supporting quotations from study texts6970### Step 6: Meta-Analysis and Evidence Synthesis7172Perform quantitative synthesis when studies are sufficiently homogeneous:73- Calculate standardized effect sizes (SMD, OR, RR, HR as appropriate)74- Fit random-effects or fixed-effects meta-analysis models75- Generate forest plots with study-level and pooled estimates76- Assess heterogeneity: I-squared statistic, Cochran's Q test, tau-squared77- Subgroup and sensitivity analyses: leave-one-out, trim-and-fill, funnel plots7879### Step 7: PRISMA Reporting and Final Output8081Compile the review following PRISMA 2020 reporting standards:82- PRISMA flow diagram with identification, screening, eligibility, inclusion counts83- Characteristics of included studies table84- Risk-of-bias summary and individual study assessments85- Forest plots, funnel plots, and subgroup analysis figures86- Summary of findings table with GRADE certainty ratings87- Complete PRISMA 2020 checklist (Page et al., BMJ 2021;372:n71) cross-referenced88 to report sections8990## Integration Points9192- **literature-search** -- Multi-database querying, deduplication, citation chaining, export93- **scienceclaw-ie** -- Structured data extraction from PDFs, entity recognition, table parsing94- **paper-writing** -- PRISMA-compliant report generation, figure formatting, reference management9596## Output Formats9798- **PRISMA flow diagram**: Study counts at each screening stage with exclusion reasons99- **Study characteristics table**: Design, population, intervention, outcomes per study100- **Forest plot**: Effect sizes with CIs, weights, pooled estimate, heterogeneity stats101- **Risk-of-bias table**: Domain-level judgments per study with traffic light visualization102- **Summary of findings**: GRADE-rated evidence table for each outcome103- **Full report**: PRISMA 2020 compliant manuscript with all required sections104105## PRISMA 2020 Checklist Reference106107This workflow aligns with the PRISMA 2020 statement (Page et al., BMJ1082021;372:n71). The 27-item checklist spans title through other information,109and each workflow step maps to specific checklist items to ensure completeness.110111## Best Practices1121131. Register the protocol before beginning searches to reduce reporting bias1142. Use at least two independent reviewers for screening and extraction1153. Document every decision point for full transparency and reproducibility1164. Never modify inclusion criteria after seeing search results without justification1175. Report all pre-planned analyses regardless of statistical significance1186. Use GRADE to rate certainty of evidence for each outcome separately1197. Clearly distinguish direct evidence from indirect comparisons1208. Acknowledge limitations in study-level quality and review-level methodology1219. Update the review when substantial new evidence becomes available12210. Make extracted data and analysis code publicly available when possible123124## Input Validation125126This skill accepts requests that match the documented purpose of `systematic-review` and include enough context to complete the workflow safely.127128Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:129130> `systematic-review` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.