Matrixx0070 When to use
Use this when the user needs a defensible summary of what is known about a gene, pathway, disease mechanism, modality, or method, with traceable citations rather than unsourced prose. Reach for it before target work, grant writing, or experiment design.
Not for: ranking drug targets (use bio-target-prioritization), choosing what to work on (use bio-problem-selection), or answering a single factual lookup where one authoritative reference already settles it.
Method
- Frame the question in PICO terms: population/system, intervention/perturbation, comparator, outcome. State species scope and time window.
- Build a query set: MeSH terms, free-text synonyms, HGNC gene aliases, and preprint spellings. Decision: if aliases are ambiguous (e.g. a symbol reused across species), split into separate queries rather than OR-ing them.
- Search PubMed/PMC for peer-reviewed work and bioRxiv/medRxiv for preprints. Tag preprints as unreviewed at capture time.
- Screen by title/abstract, then full text. Record inclusion/exclusion counts so the search is reproducible.
- Extract per paper into an evidence table: design, model system, sample size, effect direction/magnitude, key caveats.
- Grade each claim. Decision: strong = multiple independent in vivo/clinical; moderate = single robust study or consistent in vitro; weak = preprint, correlational, or contested. If two strong papers disagree, log a contradiction — do not average them away.
- Synthesize: consensus, open controversies, and gaps that motivate new work.
Example
Question: "Does TREM2 loss-of-function increase Alzheimer's risk?" You query TREM2 AND (Alzheimer OR "amyloid") AND (variant OR knockout), screen 60 hits to 14, and build the table. Human genetics (R47H GWAS/Mendelian, multiple cohorts) grades strong; mouse amyloid-clearance studies grade moderate and split on direction depending on disease stage. Output flags the stage-dependence as an open controversy rather than a settled effect.
Pitfalls
- Citing an abstract you never opened — confabulated PMIDs are the top failure. Verify every identifier resolves.
- Treating a preprint as equivalent to peer-reviewed evidence.
- Missing papers because you searched the current gene symbol only and skipped historical aliases.
- Burying a contradiction inside "studies suggest" instead of naming both sides.
Output format
# <Question> — Evidence Review
Executive summary: <one paragraph>
## Evidence table
| Claim | Grade (strong/mod/weak) | Key refs (PMID/DOI) | Caveat |
## Synthesis (by sub-question)
- <consensus / controversy per group>
## Contradictions & gaps
- <conflicting findings, missing studies>
## References
1. Author, Year. Title. PMID/DOI. [preprint if applicable]
Never invent a citation. If a claim is unsupported, say so and mark it UNVERIFIED.
Reference
Evidence hierarchy (adapted from GRADE and OCEBM)
GRADE grades a body of evidence, not a single paper, and rates certainty in four levels. Start high for RCT-type designs and low for observational, then move up or down.
| Certainty | Meaning | Typical basis |
|---|---|---|
| High ⊕⊕⊕⊕ | Further research very unlikely to change the estimate | Consistent, low-bias RCTs or large Mendelian-randomization + replicated GWAS |
| Moderate ⊕⊕⊕ | Further research may change the estimate | Single good RCT, or downgraded observational with large effect |
| Low ⊕⊕ | Estimate is uncertain | Observational, small n, indirect model |
| Very low ⊕ | Any estimate is very uncertain | Case reports, mechanistic-only, contested preprints |
GRADE downgrades for: (1) risk of bias, (2) inconsistency (heterogeneity, I² high), (3) indirectness (surrogate endpoint, wrong population/species), (4) imprecision (wide CI crossing null), (5) publication bias (funnel asymmetry). GRADE upgrades observational data for: large effect (RR >2 or <0.5), dose-response gradient, and plausible confounding that would only strengthen the effect. For a preclinical mechanism review, map "human genetics + in-vivo replication" → high; "single in-vitro" → low.
Classic OCEBM levels for orientation: 1 = systematic review of RCTs; 2 = individual RCT; 3 = non-randomized cohort/case-control; 4 = case series; 5 = mechanism-based reasoning. Preprints inherit the design's level but carry a standing "risk of bias: not peer reviewed" flag until published.
PubMed / PMC search syntax
Field tags (append in [...]):
| Tag | Field | Example |
|---|---|---|
[mesh] |
MeSH term (exploded by default) | alzheimer disease[mesh] |
[majr] |
MeSH major topic | neoplasms[majr] |
[mesh:noexp] |
MeSH, no explosion | dementia[mesh:noexp] |
[tiab] |
Title/abstract free text | TREM2[tiab] |
[ti] |
Title only | microglia[ti] |
[tw] |
Text word (broad) | amyloid[tw] |
[au] |
Author | Karran E[au] |
[dp] |
Date of publication | 2018:2024[dp] |
[pt] |
Publication type | randomized controlled trial[pt] |
[la] |
Language | english[la] |
[sb] |
Subset filter | medline[sb] |
Boolean operators are uppercase AND / OR / NOT; parentheses nest; double-quotes force phrase search; * truncates (min 4 leading chars, e.g. inflamm*). MeSH auto-explodes to narrower terms unless you use :noexp. "Automatic Term Mapping" silently expands untagged words — tag terms explicitly for reproducible queries. Use the #1 AND #2 History syntax to combine numbered searches. PMC full-text adds [body] and [fulltext]. bioRxiv/medRxiv have no MeSH — rely on [tiab]/keyword and their category facets, and always resrecord the version (v1/v2) since preprints mutate.
Reporting standards to cite by study type
PRISMA (systematic reviews, 27-item checklist + flow diagram of identified→screened→included), CONSORT (RCTs), STROBE (observational), ARRIVE 2.0 (in-vivo animal studies — the "essential 10" incl. sample size, randomization, blinding), MIAME/MINSEQE (array/seq data), STARD (diagnostic accuracy). Note which standard a paper claims to follow; deviation is a bias signal.
Identifier hygiene
PMID = integer, resolves at pubmed.ncbi.nlm.nih.gov/<id>. PMCID = PMC + digits (full text). DOI = 10.xxxx/..., resolves at doi.org/<doi>. Preprint DOIs from bioRxiv are 10.1101/<date>.<id>. Verify every identifier resolves to the title you cite — a plausible-looking but wrong PMID is the single most common confabulation failure. Gene symbols: reconcile against HGNC (genenames.org) and record aliases/prior symbols so historical papers aren't missed.