Evidence map›Paper›PMID 37109197›Full record

ArticleJournal of clinical medicine2023

Treatment Changes and Prognoses in Patients with Incident Drug-Induced Parkinsonism Using a Korean Nationwide Healthcare Claims Database.

Siin Kim, Hae Sun Suh

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 37% of its field
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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
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

2 authors at 1 institution in 1 country.

Siin KimCollege of Pharmacy, Kyung Hee University, Seoul 02447, Republic of Korea.ORCID 0000-0002-4368-7389
Hae Sun SuhCollege of Pharmacy, Kyung Hee University, Seoul 02447, Republic of Korea.ORCID 0000-0003-3445-6607
Kyung Hee University · KR

Funding

Ministry of Food and Drug Safety 21153MFDS601National Research Foundation of Korea NRF-2020R1F1A1069526
6 · The paper itself

Abstract

This retrospective cohort study assessed treatment changes and prognoses after incident drug-induced parkinsonism (DIP). We used the National Health Insurance Service's National Sample Cohort database in South Korea. We selected patients diagnosed with incident DIP and given prescriptions to take offending drugs (antipsychotics, gastrointestinal (GI) motility drugs, or flunarizine) for a period of time that overlapped with the time of DIP diagnosis during 2004-2013. The proportion of patients experiencing each type of treatment change and prognosis was assessed for 2 years after DIP diagnosis. We identified 272 patients with incident DIP (51.9% of patients were aged ≥ 60 years and 62.5% of them were women). Switching (38.4%) and reinitiation (28.8%) were the most common modifications in GI motility drug users, whereas dose adjustment (39.8%) and switching (23.0%) were common in antipsychotic users. The proportion of persistent users was higher among antipsychotic users (7.1%) than that among GI motility drug users (2.1%). Regarding prognosis, 26.9% of patients experienced DIP recurrence or persistence, the rate being the highest in persistent users and the lowest in patients who discontinued the drug. Among patients with incident DIP diagnoses, the patterns of treatment change and prognosis differed across the types of offending drugs. Over 25% of patients experienced DIP recurrence or persistence, highlighting the need for an effective strategy to prevent DIP.

Indexed as

antipsychoticsdrug-induced parkinsonismmetoclopramideparkinsonismprognosistreatment pattern

Identifiers

PMID37109197
PMCPMC10145513
OpenAlexW4365514844

What OpenQuestion holds

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LicenceCC BY
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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.