Evidence map›Paper›PMID 38976124›Full record

ArticleAdvances in therapy2024

Risk for Mortality in High versus Low Antiparkinsonian Therapy Dose During the First Year of Parkinson's Disease: A Real-World Study.

Yael Barer, Olga Sánchez-Soliño, Gabriel Chodick, Meital Grabarnik-John, Shiran Naftelberg Blonder, Neta Li Feurestein-Ganor, Lars Bergmann, Connie H Yan, Sivan Gazit, David Arkadir

Abstract read
In one paragraph

Article in Advances in therapy, 2024. 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
–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

1 citing paper in PubMed.

  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

10 authors.

Yael BarerMaccabi Institute for Research and Innovation, Maccabi Healthcare Services, Ha'Mered 27, 6812509, Tel Aviv, Israel. barer_y@mac.org.il.ORCID 0000-0002-3264-3380
Olga Sánchez-SoliñoAbbVie Inc., North Chicago, IL, USA.
Gabriel ChodickMaccabi Institute for Research and Innovation, Maccabi Healthcare Services, Ha'Mered 27, 6812509, Tel Aviv, Israel.
Meital Grabarnik-JohnAbbVie Inc., Hod Hasharon, Israel.
Shiran Naftelberg BlonderAbbVie Inc., Hod Hasharon, Israel.
Neta Li Feurestein-GanorAbbVie Inc., Hod Hasharon, Israel.
Lars BergmannAbbVie Inc., North Chicago, IL, USA.
Connie H YanAbbVie Inc., North Chicago, IL, USA.
Sivan GazitMaccabi Institute for Research and Innovation, Maccabi Healthcare Services, Ha'Mered 27, 6812509, Tel Aviv, Israel.
David ArkadirDepartment of Neurology and the Faculty of Medicine, Hadassah Medical Center, The Hebrew University, Jerusalem, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionEarly, simple predictors for long-term survival in Parkinson's disease (PD) may help identify patients at elevated risk and are crucial for more personalized treatment.

methodsThis large, retrospective study examined whether higher levodopa equivalent daily dose (LEDD) a year after diagnosis predicts long-term survival.

resultsMortality risk was increased among 292 patients receiving ≥ 600 mg LEDD versus 2233 patients receiving < 600 mg LEDD (hazard ratio 1.5; 95% confidence interval 1.3-1.7), particularly among patients aged < 75 years (1.8; 1.4-2.4).

conclusionIn PD, higher LEDD can be an early risk marker of increased mortality, probably because it reflects more severe disease.

Indexed as

Antiparkinson AgentsLevodopaParkinson DiseaseAgedAged, 80 and overDose-Response Relationship, DrugFemaleHumansMaleMiddle AgedRetrospective StudiesRisk FactorsAntiparkinson AgentsLevodopaAntiparkinson’s agentsDrug dosage calculationsMortalityParkinson’s disease

Identifiers

PMID38976124
PMCPMC11263234

What OpenQuestion holds

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LicenceCC BY-NC
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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.