systematic-review

<search_backend> For database search execution, use the CLI backends owned by the literature-review skill, located in its scripts/ directory. Invoke each by its absolute path (uv run <literature-review-dir>/scripts/X.py …); never cd into the skill directory. Anchor the review workspace with an absolute --workspace "$(pwd)/review/{slug}" under the directory where the user invoked the skill — never relative, which would write into the installed plugin.

Prerequisite — uv must be installed. Run bash <plugin-root>/scripts/setup.sh once. See the literature-review skill's <search_backend> section for full backend details, invocation patterns, and fallback install instructions.

Source	Script	Role in PRISMA
OpenAlex	openalex_cli.py	Primary cross-disciplinary database — citation counts, author/institution metadata
Europe PMC	europepmc_api.py	Life-science full text; forward/backward citation chaining; preprint coverage via SRC:PPR
arXiv	search_arxiv.py	Grey literature for CS/physics/quant-bio preprints
Full text	read_paper.py	Retrieval for eligibility assessment and extraction; logs abstract-only for "reports not retrieved" in the PRISMA flow

For each database, record verbatim:

1. The exact query string
2. The date executed
3. The total hit count (hitCount field for Europe PMC, length of results for OpenAlex/arXiv after pagination)

This metadata feeds the PRISMA flow diagram and the supplementary search log required for publication.

All review state lives in review/{slug}/ exactly as defined in the literature-review skill's protocol: protocol.md, corpus.json, papers/{id}/, synthesis.md. corpus.json is the source of truth for every PRISMA flow count. Keep it current as you go: every screening decision needs a status and, when excluded, a reason; every retrieved paper needs read_paper.py's status written into its fulltext field. Records left at null are counted as unscreened or not retrieved, and the flow numbers will silently under-report. </search_backend>

1. Protocol development (PROSPERO-ready)

• PICOTS framework: Population, Intervention, Comparison, Outcomes, Timing, Setting.
• Search logic: Exhaustive term expansion (MeSH + Emtree synonyms + free-text); translate the same Boolean intent into each backend's syntax.

2. PRISMA 2020 execution

• Flow diagram: Track Identification → Screening → Eligibility → Inclusion with hit counts per database.
• Deduplication: Cross-database dedup by DOI, then by normalized title + first-author surname + year.

3. Risk of Bias analysis

• Tools: Cochrane RoB 2.0 (RCTs), ROBINS-I (non-randomized), QUADAS-2 (diagnostic accuracy).
• Synthesis decision: Quantitative meta-analysis only when heterogeneity (I²) and effect-measure compatibility permit; otherwise structured qualitative synthesis.

<output_format>

Systematic Review: [Question]

PRISMA phase: [Identification | Screening | Eligibility | Included | Synthesis] PICO(TS): P=… I=… C=… O=… T=… S=…

Search log:

Database	Query	Date	Hits
OpenAlex	…	YYYY-MM-DD	N
Europe PMC	…	YYYY-MM-DD	N
arXiv	…	YYYY-MM-DD	N

PRISMA flow: take "Identified" from protocol.md's logged per-database hit counts. Generate the remaining counts (after dedup, screened, excluded, retrieval, included) with uv run <literature-review-dir>/scripts/prisma_counts.py --corpus "$WS/corpus.json" — never hand-count; the script exits 1 if any exclusion lacks a reason.

• Identified: N (after dedup: N)
• Screened (title/abstract): N → excluded N (reasons in corpus.json)
• Sought for retrieval: N → not retrieved N (abstract-only)
• Full-text assessed: N → excluded N (reasons logged)
• Included: N

Evidence table:

Study ID	Design	N	RoB	Key outcome	GRADE

Next PRISMA steps:

1. [Step]
2. [Step] </output_format>