Systematic Review Screener

Automated abstract screening tool for systematic literature reviews with PRISMA workflow support.

Automated abstract screening tool for systematic literature reviews with PRISMA workflow support.

When to Use

• Use this skill when the task needs Automated abstract screening tool for systematic literature reviews with PRISMA workflow support.
• Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

See ## Features above for related details.

• Scope-focused workflow aligned to: Automated abstract screening tool for systematic literature reviews with PRISMA workflow support.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

See ## Prerequisites above for related details.

• Python: 3.10+. Repository baseline for current packaged skills.
• dataclasses: unspecified. Declared in requirements.txt.
• yaml: unspecified. Declared in requirements.txt.

Example Usage

See ## Usage above for related details.

bash

cd "20260318/scientific-skills/Evidence Insight/systematic-review-screener"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

bash

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

bash

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py -h
python scripts/main.py --help

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Overview

This skill screens academic abstracts against predefined inclusion/exclusion criteria, generating PRISMA-compliant outputs with decision rationale and confidence scores.

Technical Difficulty: High ⚠️ Manual verification recommended for final inclusion decisions.

Features

• Multi-format Input: PubMed MEDLINE, EndNote XML, CSV/TSV
• Criteria Matching: Configurable inclusion/exclusion rules
• Confidence Scoring: 0-100% confidence for each decision
• Conflict Detection: Flags abstracts requiring human review
• PRISMA Export: Flow diagram data and screening log
• Batch Processing: Handles large reference sets efficiently

Usage

Basic Screening

python

# Run with default settings
python scripts/main.py --input references.csv --criteria criteria.yaml

With PRISMA Export

python

python scripts/main.py --input references.xml --criteria criteria.yaml \
  --output results/ --prisma --format excel

Confidence Threshold

python

python scripts/main.py --input refs.txt --criteria criteria.yaml \
  --threshold 0.8 --conflict-only

Input Formats

1. CSV/TSV

Required columns: title, abstract (optional: authors, year, doi, pmid)

csv

title,abstract,authors,year
title,abstract,authors,year

2. PubMed MEDLINE

Standard .txt export from PubMed search.

3. EndNote XML

Export from EndNote with abstracts included.

Criteria File (YAML)

See references/criteria_template.yaml for complete example:

yaml

study_type:
  include:
    - "randomized controlled trial"
    - "systematic review"
  exclude:
    - "case report"
    - "letter"
    - "editorial"

population:
  include_keywords:
    - "adults"
    - "elderly"
  exclude_keywords:
    - "pediatric"
    - "children"

intervention:
  required:
    - "drug therapy"
    - "medication"

language:
  allowed: ["English"]
  
year_range:
  min: 2010
  max: 2024

confidence_threshold: 0.75

Output Files

File	Description
screened_included.csv	Records passing all criteria
screened_excluded.csv	Records failing one or more criteria
conflicts.csv	Low-confidence decisions requiring review
prisma_data.json	PRISMA flow diagram counts
screening_log.json	Full decision trail with rationale

PRISMA Workflow Support

Generates structured data for PRISMA 2020 flow diagram:

json

{
  "identification": {
    "database_results": 1250,
    "register_results": 45,
    "other_sources": 12
  },
  "screening": {
    "records_screened": 1307,
    "records_excluded": 1150,
    "full_text_assessed": 157,
    "full_text_excluded": 89
  },
  "included": {
    "qualitative_synthesis": 68,
    "quantitative_synthesis": 42
  }
}

Configuration

Environment Variables

text

export SCREENING_THRESHOLD=0.75  # Default confidence threshold
export BATCH_SIZE=100             # Records per batch
export MAX_WORKERS=4              # Parallel processing workers

Command Line Options

Option	Description	Default
--input	Input file path	Required
--criteria	Criteria YAML path	Required
--output	Output directory	./output
--format	Output format: csv/excel/json	csv
--threshold	Confidence threshold	0.75
--prisma	Generate PRISMA data	False
--conflict-only	Export only conflicts	False
--batch-size	Processing batch size	100

Decision Algorithm

1. Keyword Matching: Exact and fuzzy keyword matching against title/abstract
2. Inclusion Scoring: Points for each inclusion criterion matched
3. Exclusion Check: Immediate exclusion if exclusion criterion detected
4. Confidence Calculation: Weighted score based on keyword presence and clarity
5. Conflict Flagging: Records with confidence < threshold flagged for manual review

Limitations

• Not for Final Decisions: Tool provides recommendations; human review required for inclusion
• Language Dependent: Optimized for English abstracts
• Structured Abstracts: Performs better on structured abstracts (Background/Methods/Results/Conclusion)
• Domain Specific: Criteria must be tailored to research question

References

• references/criteria_template.yaml - Complete criteria configuration example
• references/prisma_2020_checklist.pdf - PRISMA 2020 reporting guidelines
• references/sample_references.csv - Example input format

Version

Version: 1.0.0
Last Updated: 2026-02-05
Classification: Research Tool - Requires Human Verification

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

text

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of systematic-review-screener 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-screener only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.