1.1 What ORISMA does

ORISMA automates seven analytical steps:

1. Ingestion – reads RIS/BibTeX/CSV files from multiple databases
2. Deduplication – three-step pipeline (DOI + title + fuzzy matching)
3. Risk extraction – dictionary-based classification into 58 normative categories
4. Bibliometric analysis – computes WRDI, RCS, and MGP indicators
5. Dimension detection – automatic discovery of normative blocks (A-F)
6. Preventive intelligence – Abstract Sufficiency Score (ASS) and bridge article detection
7. Report generation – bilingual HTML reports, risk sheets, extraction matrices

1.2 Installation

# From CRAN
install.packages("orisma")

# Development version
remotes::install_github("Aguilar-Elena/orisma")

3.1 Loading references

orm_load() reads all RIS, BibTeX, and CSV files from a folder, detects the source database automatically, and returns a standardised orisma_refs object.

library(orisma)

refs <- orm_load(
  path = "path/to/ris/files/",
  lang = "en"
)

In the metal AM corpus, two files were loaded:

• Wos.ris – 73 records from Web of Science
• scopusris.ris – 111 records from Scopus
• Total: 184 records identified

3.2 Deduplication

orm_dedup() runs a three-step deduplication pipeline:

1. Exact DOI match – records with identical DOIs are deduplicated
2. Normalised title match – titles are lowercased, stripped of punctuation, and matched exactly
3. Fuzzy match – remaining potential duplicates are identified using Levenshtein distance with a configurable threshold (default: 0.90 similarity)

# Deduplication runs automatically inside orm_run()
# but can be called independently:
deduped <- orm_dedup(refs, fuzzy_threshold = 0.90, verbose = TRUE)

Result for the metal AM corpus:

70 duplicates (38.0%) were removed, yielding 114 unique records. The high overlap between WoS and Scopus confirms the importance of multi-database deduplication in systematic reviews of this topic.

4.1 The ORISMA dictionary

Risk categories are extracted using a built-in normative dictionary of 58 categories organised in six blocks anchored in ISO 45001:2018, INSST, NIOSH, and EU-OSHA:

dict <- orm_dict()
orm_dict_categories(dict)

The dictionary can be extended for any domain:

# Add terms to an existing category
dict <- orm_dict_add_terms(dict, "nanomaterials",
                           c("nano-aerosol", "NOAA particle"))

# Add a completely new category
dict <- orm_dict_add_category(dict,
  key      = "laser_safety",
  label_en = "Laser safety in AM processes",
  label_es = "Seguridad laser en procesos AM",
  terms    = c("laser safety", "laser hazard", "laser exposure")
)

4.2 Extraction results

mx <- orm_extract(deduped, dict = dict,
                  fields  = c("title", "abstract", "keywords"),
                  verbose = TRUE)
print(mx)

22 of 58 categories were detected in the metal AM corpus. The top five by frequency:

Note that “Additive manufacturing and 3D printing” (Block F) dominates the corpus – this is the technological context of the corpus, not a risk category per se. ORISMA’s orm_priority() function automatically separates context categories from risk categories.

5.1 Worker-Risk Disconnection Index (WRDI)

The WRDI measures the proportion of studies characterising an occupational risk without including direct worker exposure data. It is computed at both the category level and globally.

\[WRDI_c = 1 - \frac{N_{workers,c}}{N_{total,c}}\]

where \(N_{workers,c}\) is the number of studies in category \(c\) that include worker exposure terms (e.g. “worker exposure”, “breathing zone”, “personal sampling”, “field study”) and \(N_{total,c}\) is the total number of studies.

result <- orm_run(refs, topic = "Metal additive manufacturing - OHS")
cat("Global WRDI:", result$WRDI_global, "\n")

Global WRDI = 0.6053 for the metal AM corpus. This means that 60.5% of studies characterise occupational risks without including direct worker exposure data. The literature is predominantly technical – characterising emissions, particles, and processes in controlled settings – without measuring what workers actually breathe or absorb.

WRDI by category reveals important heterogeneity:

5.2 Risk Category Saturation Index (RCS)

The RCS measures the relative dominance of each risk category compared to a hypothetical uniform distribution:

\[RCS_c = \frac{N_c \cdot K}{N_{total}}\]

where \(K\) = 58 categories. RCS > 1 indicates over-representation; RCS < 1 indicates under-representation.

ind <- result$indicators[result$indicators$n_records > 0, ]
ind[order(-ind$RCS), c("label", "n_records", "RCS")]

The metal AM corpus shows extreme saturation:

• Additive manufacturing and 3D printing: RCS = 24.1 – massively over-represented (the dominant topic of the corpus)
• Exposure to hazardous chemical agents: RCS = 13.0 – strongly over-represented
• Most other categories: RCS < 1 – under-represented or absent

This pattern confirms that the literature has concentrated on chemical emissions while largely ignoring safety, ergonomic, psychosocial, and biological risks.

5.3 Material-Gap Profile (MGP)

The MGP identifies materials that are hazardous but understudied:

\[MGP_m = \frac{hazard\_proxy_m}{coverage_m}\]

print(result$MGP)

Titanium has the highest MGP (45.3). Despite its known carcinogenic and respiratory hazard potential, it appears in only 2.9% of corpus studies. This represents the most critical material gap in the field.

7.1 Abstract Sufficiency Score (ASS)

The ASS is a cumulative 0-5 index of preventive informativeness:

mx  <- orm_ass(result$mx, verbose = TRUE)
orm_ass_plot(mx, lang = "en")

Distribution for the metal AM corpus (N=114):

Mean ASS = 2.04/5. Only 3 abstracts (2.6%) contain complete preventive information. Nearly one quarter (24.6%) contain no OHS-useful information at all. This quantifies the practical gap that practitioners face when consulting the primary literature.

7.2 Bridge article detection

mx <- orm_bridge(result$mx, verbose = TRUE)

Bridge classification:

77.2% of studies are purely technical – they do not simultaneously address workers, exposure measurement, AND preventive recommendations. Only 22 studies (19.3%) qualify as strong bridges between technical science and applied OHS prevention.

7.3 Priority reading ranking

ranking <- orm_ranking(result$mx, top_n = 10, lang = "en")
print(ranking)

The top-ranked article (Additive Manufacturing for Occupational Hygiene, Stefaniak et al. 2021) achieves a perfect bridge score of 5/5 and ASS of 5/5, with a combined priority score of 21. This is the single most valuable article for a practitioner entering this field.

8.1 Risk sheet

The orm_risk_sheet() function generates a structured, actionable risk sheet for OHS practitioners. It is regulation-neutral (applicable globally) and includes the traffic-light priority classification:

orm_risk_sheet(result,
  topic           = "Metal additive manufacturing",
  search_strategy = "Systematic search in WoS and Scopus (2015-2026).",
  out_dir         = "outputs/",
  lang            = "en"
)

The risk sheet:

• Separates context categories (RCS > 15, dominant topic) from actual risk categories
• Classifies each risk as RED (critical gap), AMBER (attention), GREEN (reasonable coverage), or GREY (insufficient evidence)
• Includes a full methodological section with WRDI definition, extraction method, deduplication statistics, and limitations

8.2 Guided extraction matrix

For systematic reviewers, orm_extraction_matrix() generates a pre-filled CSV template ready for full-text review:

orm_extraction_matrix(result$mx, result,
  top_n   = 20,
  out_dir = "outputs/",
  lang    = "en"
)

The matrix pre-fills bibliographic data, ORISMA scores, auto-detected technology, and risk categories. Reviewers only need to complete the PDF-dependent fields (study design, population, exposure level, main result, preventive recommendations).