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Development of a European Union Time-Indexed Reference Dataset for Assessing the Performance of Signal Detection Methods in Pharmacovigilance using a Large Language Model

Maria Kefala, Jeffery L. Painter, Syed Tauhid Bukhari, Maurizio Sessa · Mar 27, 2026 · Citations: 0

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Data freshness

Extraction: Fresh

Check recency before relying on this page for active eval decisions. Use stale pages as context and verify against current hub results.

Metadata refreshed

Mar 27, 2026, 3:53 PM

Recent

Extraction refreshed

Apr 10, 2026, 7:24 AM

Fresh

Extraction source

Persisted extraction

Confidence 0.15

Abstract

Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets. Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses to pre-confirmation periods and limiting evaluation of early detection performance. This study addresses this gap by developing a time-indexed reference dataset for the European Union (EU), incorporating the timing of AE inclusion in product labels along with regulatory metadata. Methods: Current and historical Summaries of Product Characteristics (SmPCs) for all centrally authorized products (n=1,513) were retrieved from the EU Union Register of Medicinal Products (data lock: 15 December 2025). Section 4.8 was extracted and processed using DeepSeek V3 to identify AEs. Regulatory metadata, including labelling changes, were programmatically extracted. Time indexing was based on the date of AE inclusion in the SmPC. Results: The database includes 17,763 SmPC versions spanning 1995-2025, comprising 125,026 drug-AE associations. The time-indexed reference dataset, restricted to active products, included 1,479 medicinal products and 110,823 drug-AE associations. Most AEs were identified pre-marketing (74.5%) versus post-marketing (25.5%). Safety updates peaked around 2012. Gastrointestinal, skin, and nervous system disorders were the most represented System Organ Classes. Drugs had a median of 48 AEs across 14 SOCs. Conclusions: The proposed dataset addresses a critical gap in pharmacovigilance by incorporating temporal information on AE recognition for the EU, supporting more accurate assessment of signal detection performance and facilitating methodological comparisons across analytical approaches.

Low-signal caution for protocol decisions

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • Extraction flags indicate low-signal or possible false-positive protocol mapping.
  • Extraction confidence is 0.15 (below strong-reference threshold).
  • No explicit evaluation mode was extracted from available metadata.
  • No benchmark/dataset or metric anchors were extracted.
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HFEPX Relevance Assessment

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

Background context only.

Main weakness

Extraction flags indicate low-signal or possible false-positive protocol mapping.

Trust level

Low

Eval-Fit Score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

HFEPX Fit

Adjacent candidate

Extraction confidence: Low

If you are doing eval pipeline work, start here:

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Field Provenance & Confidence

Each key protocol field shows extraction state, confidence band, and data source so you can decide whether to trust it directly or validate from full text.

Human Feedback Types

missing

None explicit

Confidence: Low Source: Persisted extraction missing

No explicit feedback protocol extracted.

Evidence snippet: Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets.

Evaluation Modes

missing

None explicit

Confidence: Low Source: Persisted extraction missing

Validate eval design from full paper text.

Evidence snippet: Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets.

Quality Controls

missing

Not reported

Confidence: Low Source: Persisted extraction missing

No explicit QC controls found.

Evidence snippet: Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets.

Benchmarks / Datasets

missing

Not extracted

Confidence: Low Source: Persisted extraction missing

No benchmark anchors detected.

Evidence snippet: Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets.

Reported Metrics

missing

Not extracted

Confidence: Low Source: Persisted extraction missing

No metric anchors detected.

Evidence snippet: Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets.

Rater Population

missing

Unknown

Confidence: Low Source: Persisted extraction missing

Rater source not explicitly reported.

Evidence snippet: Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets.

Human Data Lens

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Unknown
  • Unit of annotation: Unknown
  • Expertise required: General
  • Extraction source: Persisted extraction

Evaluation Lens

  • Evaluation modes:
  • Agentic eval: None
  • Quality controls: Not reported
  • Confidence: 0.15
  • Flags: low_signal, possible_false_positive

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Deterministic synthesis

Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses to pre-confirmation periods and limiting evaluation of early detection performance. HFEPX protocol signal is limited in abstract-level metadata, so treat it as adjacent context. Updated from current HFEPX corpus.

Generated Apr 10, 2026, 7:24 AM · Grounded in abstract + metadata only

Key Takeaways

  • Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses to pre-confirmation periods and…
  • Most AEs were identified pre-marketing (74.5%) versus post-marketing (25.5%).

Researcher Actions

  • Treat this as method context, then pivot to protocol-specific HFEPX hubs.
  • Identify benchmark choices from full text before operationalizing conclusions.
  • Verify metric definitions before comparing against your eval pipeline.

Caveats

  • Generated from title, abstract, and extracted metadata only; full-paper implementation details are not parsed.
  • Low-signal flag detected: protocol relevance may be indirect.

Research Summary

Contribution Summary

  • Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses to pre-confirmation periods and limiting evaluation of early detection performance.
  • Most AEs were identified pre-marketing (74.5%) versus post-marketing (25.5%).
  • Safety updates peaked around 2012.

Why It Matters For Eval

  • Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses to pre-confirmation periods and limiting evaluation of early detection performance.
  • Safety updates peaked around 2012.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Gap: Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

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