Systematic Review Screener

• 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.

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.

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.

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

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

With PRISMA Export

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

Confidence Threshold

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)

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:

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