Evidence map›Paper›PMID 42572160›Full record

ArticleClinical pharmacology and therapeutics2026

Prompt Engineering of Large Language Models for Medication Dose Calculation.

Max Weaver, Natasha J Petry, Jeremy Cauwels, Roxana A Lupu, Amanda Massmann

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical pharmacology and therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Max WeaverSanford Imagenetics, Sanford Health, Sioux Falls, South Dakota, USA.ORCID https://orcid.org/0009-0009-1351-9972
Natasha J PetrySanford Imagenetics, Sanford Health, Sioux Falls, South Dakota, USA.ORCID https://orcid.org/0000-0001-5089-9286
Jeremy CauwelsSanford USD Medical Center, Sanford Health, Sioux Falls, South Dakota, USA.
Roxana A LupuSanford USD Medical Center, Sanford Health, Sioux Falls, South Dakota, USA.ORCID https://orcid.org/0000-0002-6316-1835
Amanda MassmannSanford Imagenetics, Sanford Health, Sioux Falls, South Dakota, USA.ORCID https://orcid.org/0000-0002-5401-5318

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to design and validate a large language model (LLM) framework for systematic extraction of medication dosing across multiple therapeutic classes using electronic health record (EHR) data. Manual dose annotations were completed for 4295 medications across nine therapeutic classes. Five publicly available LLMs (Mistral-small, Llama 3-70B, Nova lite, DeepSeek, and Claude 3.5 Sonnet) were tested through iterative prompt engineering. Discrepancies between manual and model-derived doses were assessed using R

Indexed as

Drug Dosage CalculationsElectronic Health RecordsLarge Language ModelsHumans

Identifiers

PMID42572160
PMCPMC13454412

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.