Evidence map›Paper›PMID 41741128›Full record

ArticleBMJ mental health2026

Automating thematic review of prevention of future deaths reports: concordance study of a child-suicide analysis using large language models.

Sam Osian, Arpan Dutta, Sahil Bhandari, Iain E Buchan, Dan W Joyce

Abstract read
In one paragraph

Article in BMJ mental health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Sam OsianDepartment of Primary Care and Mental Health, Civic Health Innovation Labs and Mental health Research for Innovation Centre, University of Liverpool, Liverpool, UK samoand@liverpool.ac.uk.ORCID http://orcid.org/0009-0000-7644-6218
Arpan DuttaMental health Research for Innovation Centre, Mersey Care NHS Foundation Trust, University of Liverpool, Liverpool, UK.ORCID http://orcid.org/0000-0003-2906-3286
Sahil BhandariMental health Research for Innovation Centre, Mersey Care NHS Foundation Trust, University of Liverpool, Liverpool, UK.ORCID http://orcid.org/0009-0006-3469-9325
Iain E BuchanMental health Research for Innovation Centre, Mersey Care NHS Foundation Trust, University of Liverpool, Liverpool, UK.ORCID http://orcid.org/0000-0003-3392-1650
Dan W JoyceDepartment of Primary Care and Mental Health, Civic Health Innovation Labs and Mental health Research for Innovation Centre, University of Liverpool, Liverpool, UK.ORCID http://orcid.org/0000-0002-9433-5340

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevention of future deaths (PFD) reports issued by coroners in England and Wales identify systemic safety hazards but are difficult to analyse at scale. Reports are not machine-readable, lack consistent metadata and cannot be reliably searched or exported, meaning prior national reviews have relied on labour-intensive manual screening and coding.

objectiveTo evaluate whether a fully automated, vision-enabled large language model (LLM) pipeline (PFD Toolkit) can replicate and extend the Office for National Statistics (ONS) thematic review of child-suicide PFD reports, and to assess concordance with blinded clinical adjudication.

methodsAll PFD reports published between July 2013 and November 2023 (n=4730) were scraped from judiciary.uk and processed using PFD Toolkit, which combines optical character recognition with LLM-powered screening and thematic coding. Reports were classified for child suicide (≤18 years), addressee categories and 23 coroner-concern subthemes mirroring the ONS coding frame. Agreement was evaluated against a blinded clinical reference standard: three psychiatrists independently adjudicated a stratified sample of 146 reports (73 Toolkit-positive cases and 73 decoys), with disagreements resolved by consensus. Inter-rater reliability and index-reference agreement were quantified using kappa statistics.

findingsThe Toolkit identified 73 child-suicide PFD reports between July 2013 and November 2023, compared with 37 identified in the ONS review. 62 cases fell within the ONS analytical window, and 11 pre-dated the introduction of suicide-related tags on the PFD archive. Pre-consensus inter-rater reliability among clinicians was substantial to almost perfect (Fleiss' κ=0.75, 95% CI 0.65 to 0.84). Post-consensus agreement between the Toolkit and the clinical reference standard was substantial to almost perfect (Cohen's κ=0.93, 95% CI 0.77 to 1.00; raw agreement 97%). End-to-end screening, coding and tabulation of all reports completed in 5 min 29 s on a consumer-grade laptop.

conclusionsA national thematic review of child-suicide PFD reports can be fully automated with high concordance to expert judgement, dramatically reducing time and labour while recovering previously missed cases. CLINICAL IMPLICATIONS: Automated analysis of PFD reports enables rapid, reproducible surveillance of recurring system failures, supporting more timely public health intelligence, policy responses and learning from coronial data.

Indexed as

AdolescentMental HealthMental Health Services

Identifiers

PMID41741128
PMCPMC12970072

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.