Evidence map›Paper›PMID 40858986›Full record

ArticleNPJ digital medicine2025

Predicting opioid consumption after surgical discharge: a multinational derivation and validation study using a foundation model.

Chris Varghese, Luke Peters, Lorane Gaborit, William Xu, Kaviya Kalyanasundaram, Aya Basam, Melissa Park, Cameron Wells, Kenneth A McLean, Gabriel Schamberg and 6 more

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Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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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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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Chris VargheseDepartment of Surgery, University of Auckland, Auckland, New Zealand. cvar706@aucklanduni.ac.nz.
Luke PetersDepartment of Surgery, University of Newcastle, Newcastle, NSW, Australia.
Lorane GaboritAustralian National University, Canberra, ACT, Australia.
William XuDepartment of Surgery, University of Auckland, Auckland, New Zealand.
Kaviya KalyanasundaramUniversity of Adelaide, Adelaide, SA, Australia.
Aya BasamMonash University, Melbourne, VIC, Australia.
Melissa ParkNorthern Sydney Local Health District, Sydney, NSW, Australia.
Cameron WellsDepartment of Surgery, University of Auckland, Auckland, New Zealand.
Kenneth A McLeanDepartment of Clinical Surgery, University of Edinburgh, Edinburgh, Scotland.
Gabriel SchambergDepartment of Surgery, University of Auckland, Auckland, New Zealand.
Greg O'GradyDepartment of Surgery, University of Auckland, Auckland, New Zealand.
Deborah WrightDepartment of Surgical Sciences, University of Otago, Dunedin, New Zealand.
Jennifer MartinDepartment of Surgery, University of Newcastle, Newcastle, NSW, Australia.
Ewen HarrisonDepartment of Clinical Surgery, University of Edinburgh, Edinburgh, Scotland.
Peter PockneyDepartment of Surgery, University of Newcastle, Newcastle, NSW, Australia.
TASMAN Collaborative

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Opioids are frequently overprescribed after surgery. We applied a tabular foundation model to predict the risk of post-discharge opioid consumption. The model was trained and internally validated on an 80:20 training/test split of the 'Opioid PrEscRiptions and usage After Surgery' (ACTRN12621001451897p) study cohort, including adult patients undergoing general, orthopaedic, gynaecological and urological operations (n = 4267), with external validation in a distinct cohort of patients discharged after general surgical procedures (n = 826). The area under the receiver operator curve was 0.84 (95% confidence interval [CI] 0.81-0.88) at internal testing and 0.77 (95% CI 0.74-0.80) at external validation. Brier scores were 0.13 (95% CI 0.12-0.14) and 0.19 (95% CI 0.17-0.2). Patients with a <50% predicted risk of opioid consumption consumed a median of 0 oral morphine equivalents in the first week after surgery. Applying this model would reduce opioid prescriptions by 4.5% globally, and counterfactual modelling suggests without increasing time in severe pain (-4.3%, 95% CI -17.7 to 8.6).

Identifiers

PMID40858986
PMCPMC12381370

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