Identify and extract all transparency indicators from a PMC XML.

Takes a PMC XML and returns relevant meta-data, as well as whether the article carries each of the ten transparency indicators: Conflicts of Interest (COI), Funding, Protocol Registration, Novelty, Replication, Data sharing, Code sharing, disclosure of generative-AI use, Open-access licensing and Reporting-guideline use. Where a statement is found, the relevant text is also extracted. This is the single-call entry point; it covers the same data and code detection as [rt_data_code_pmc()], the same AI detection as [rt_ai_pmc()], the same licensing detection as [rt_oa_pmc()] and the same reporting-guideline detection as [rt_reporting_pmc()].

Usage

rt_all_pmc(filename, remove_ns = FALSE, all_meta = FALSE)

Arguments

filename: The name of the PMC XML as a string.
remove_ns: TRUE if an XML namespace exists, else FALSE (default).
all_meta: TRUE extracts all meta-data, FALSE extracts some (default).

Value

A dataframe of results. It returns the unique identifiers of the article, whether each indicator of transparency was identified (`is_coi_pred`, `is_fund_pred`, `is_register_pred`, `is_novelty_pred`, `is_replication_pred`, `is_open_data`, `is_open_code`, the year-gated `is_ai_pred`, `is_open_access` with the `oa_license`, and `is_reporting_pred` with the named `reporting_guideline`), the relevant text identified, whether it was identified through a dedicated XML tag (such variables include "pmc" in their name, e.g. “fund_pmc_source”) and whether each labelling function identified relevant text or not. The labeling functions are returned to add flexibility in how this package is used; for example, future definitions of Registration may differ from the one we used. If a labelling function returns NA it means that it was not run. `is_ai_pred` is `NA` for articles published before 2023 (see [rt_ai_pmc()]).

Examples

# \donttest{
# Path to a bundled example PMC XML file.
filepath <- system.file(
  "extdata", "PMID32171256-PMC7071725.xml", package = "rtransparency"
)

# Identify and extract meta-data and indicators of transparency.
results_table <- rt_all_pmc(filepath, remove_ns = TRUE, all_meta = TRUE)
# }