ArticleDrug and alcohol review2026
Large Language Models Accurately Identify People Who Inject Drugs From Infectious Diseases Discharge Summaries in an Australian Hospital.
Article in Drug and alcohol review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPeople who inject drugs (PWID) face a high risk for serious infections, yet International Classification of Diseases (ICD) codes fail to identify this population. Large language models (LLM) offer a promising alternative by extracting information from unstructured clinical text. This study evaluated the diagnostic performance of off-the-shelf LLMs in identifying PWID and related attributes from hospital discharge summaries.
methodsIn this cross-sectional study, discharge summaries from the Infectious Diseases service at St Vincent's Public Hospital, Sydney, between 2018 and 2022 were reviewed. A single reviewer manually annotated each de-identified summary for PWID status, drugs reported, injection recency and opioid agonist therapy. Eight LLMs (Gemma3, Llama 3.3, Mistral, Phi4, hippomistral, llama3-med [8B and 70B] and OpenBioLLM) were compared using prevalence-weighted average-F1 scores. Diagnostic metrics with bootstrapped 95% confidence intervals were calculated for each annotated category.
resultsOf 859 first admissions, manual review identified 149 (17.1%) PWID. ICD codes showed low sensitivity (≤ 0.32) but high specificity (≥ 0.97) for identifying PWID. The best-performing model (Llama 3.3) achieved a prevalence-weighted average-F1 of 0.845 (0.733, 0.927). For injecting drug use, sensitivity was 0.819 (95% CI 0.753, 0.879) and specificity 0.999 (0.996, 1.00). Identification of heroin, methamphetamine, cannabis and methadone was near perfect (F1 > 0.973), while illicit prescription opioid and benzodiazepine use were identified less accurately (F1 = 0.400 and 0.606). DISCUSSION AND
conclusionsLLMs accurately identify PWID from discharge summaries, outperforming ICD codes. Challenges remain for certain substances, underscoring the need for task-specific tuning, external validation and integration with structured data to enhance surveillance and interventions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.