Question-Driven Reviews

Research questions as objects

scimapR treats research questions as structured objects, not just query strings.

library(scimapR)

rq <- sm_question(
 text = "Does spatial transcriptomics improve outcome prediction in colorectal cancer?",
 framework = "PICO",
 population = "colorectal cancer patients",
 intervention = "spatial transcriptomics",
 outcome = "survival or recurrence"
)
print(rq)
#> 
#> ── <sm_question> ───────────────────────────────────────────────────────────────
#> ID: Q-ecd0161dbb634ba3
#> Framework: PICO
#> Created: 2026-11-03 18:20:46
#> 
#> Question:
#> Does spatial transcriptomics improve outcome prediction in colorectal cancer?
#> 
#> ── Structured fields
#> Population: colorectal cancer patients
#> Intervention: spatial transcriptomics
#> Outcome: survival or recurrence
#> 
#> Languages: en
#> 
#> ── Query strings
#> generic: (colorectal cancer patients) AND (spatial transcriptomics) AND
#> (survival or r...
#> pubmed: (colorectal cancer patients[tiab]) AND (spatial transcriptomics[tiab])
#> AND (s...
#> openalex: (colorectal cancer patients) AND (spatial transcriptomics) AND
#> (survival or r...
#> crossref: (colorectal cancer patients) AND (spatial transcriptomics) AND
#> (survival or r...

Building a corpus from a question

# In production, this hits APIs:
corpus <- sm_corpus_for_question(rq, sources = c("pubmed", "openalex"))

For this vignette, we use the synthetic example corpus:

corpus <- sm_example_corpus(with_screening = TRUE, seed = 42)

Screening

Regex-based (deterministic)

screened <- sm_screen_regex(
 corpus,
 include_terms = c("transcriptom", "spatial"),
 exclude_terms = c("mouse", "drosophila")
)
#> ✔ Regex screening: 73 included, 127 excluded.
nrow(screened$works)
#> [1] 200

LLM-grounded screening

# Requires ellmer package and an API key
screened <- sm_screen_against_question(
 corpus, rq,
 llm = ellmer::chat_anthropic(),
 stages = "title-abstract"
)

Screening summary

sm_screen_summary(corpus)
#> 
#> ── Stage: title-abstract (n=200)
#> exclude: 96 (48%)
#> include: 82 (41%)
#> uncertain: 22 (11%)
#> # A tibble: 3 × 4
#>   stage          decision      n   pct
#>   <chr>          <chr>     <int> <dbl>
#> 1 title-abstract exclude      96    48
#> 2 title-abstract include      82    41
#> 3 title-abstract uncertain    22    11

PRISMA flow

prisma <- sm_screen_prisma(corpus)
prisma$counts
#> # A tibble: 4 × 4
#>   stage          n_entering n_excluded n_remaining
#>   <chr>               <int>      <int>       <int>
#> 1 identification        200          0         200
#> 2 title-abstract        200         96         104
#> 3 full-text             104          0         104
#> 4 inclusion             104          0         104
prisma$plot

Export for Rayyan/Covidence

sm_export_rayyan(corpus, "my_corpus.ris")
sm_export_covidence(corpus, "my_corpus_covidence.ris")