Evidence map›Paper›PMID 39622789›Full record

ArticleBritish journal of clinical pharmacology2025

Inter-rater agreement for detection of potentially inappropriate medication according to explicit and implicit STOPP criteria.

Naldy Parodi López, Staffan A Svensson, Susanna M Wallerstedt

Abstract read
In one paragraph

Article in British journal of clinical pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
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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

3 authors.

Naldy Parodi LópezDepartment of Pharmacology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID https://orcid.org/0000-0001-6367-9389
Staffan A SvenssonDepartment of Pharmacology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID https://orcid.org/0000-0002-3226-9720
Susanna M WallerstedtDepartment of Pharmacology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID https://orcid.org/0000-0001-7238-1680

Funding

Swedish Research Council 2021-01308Swedish Research Council 521-2013-2639Swedish state under the agreement between the Swedish government and the county councils ALFGBG-716941Swedish state under the agreement between the Swedish government and the county councils ALFGBG-965025
6 · The paper itself

Abstract

The Screening Tool of Older Person's Prescriptions (STOPP) is used to detect potentially inappropriate medication. Version 2 includes 80 criteria, whereof two can be considered implicit as their detection primarily relies on the assessor's expertise: (A1) drugs without indication and (A2) drug treatment beyond recommended duration. To explore the inter-rater agreement for detection of explicit and implicit criteria, data on consecutive primary care patients from a previous study (n = 302, 65-99 years of age) were used, including independent assessments of the 78 explicit criteria (23 556 assessments) and the two implicit criteria (604 assessments) by two specialist physicians. Overall, 123 (0.5%) explicit and 10 (2%) implicit criteria were fulfilled according to both physicians. The positive agreement for a criterion being fulfilled was 56% for explicit and 16% for implicit criteria, with kappa values of 0.56 and 0.09. Discordant detection of furosemide and proton pump inhibitors was common using explicit and implicit criteria.

Indexed as

Inappropriate PrescribingPotentially Inappropriate Medication ListAgedAged, 80 and overFemaleHumansMaleObserver VariationPractice Patterns, Physicians'Primary Health Careagedinter‐rater agreementpotentially inappropriate medicationprimary health carereliabilitySTOPP criteria

Identifiers

PMID39622789
PMCPMC11773097

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Registered trials

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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.