Evidence map›Paper›PMID 38235365›Full record

ArticleNeurology. Genetics2023

Improvement of an External Predictive Model Based on New Information Using a Synthetic Data Approach: Application to CADASIL.

Henri Chhoa, Hugues Chabriat, Adelina Joanita Anato, Mamadou Bamba, Florent Zittoun, Sylvie Chevret, Lucie Biard

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurology. Genetics, 2023. 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
0.6field-weighted citation impact, top 30% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Association ofNeurology · 2024
    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

7 authors at 1 institution in 1 country.

Henri ChhoaFrom the ECSTRRA Team (H. Chhoa, S.C., L.B.), Université Paris-Cité, UMR1153, INSERM; Translational Neurovascular Centre (H. Chabriat), GH Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris APHP, Université Paris-Cité and DHU NeuroVasc Sorbonne Paris-Cité; UMR 1161 (H. Chabriat), INSERM; and ENSAI (A.J.A., M.B., F.Z.), Ecole d'ingénieur statistique, data science et big data, Bruz, France.
Hugues ChabriatFrom the ECSTRRA Team (H. Chhoa, S.C., L.B.), Université Paris-Cité, UMR1153, INSERM; Translational Neurovascular Centre (H. Chabriat), GH Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris APHP, Université Paris-Cité and DHU NeuroVasc Sorbonne Paris-Cité; UMR 1161 (H. Chabriat), INSERM; and ENSAI (A.J.A., M.B., F.Z.), Ecole d'ingénieur statistique, data science et big data, Bruz, France.ORCID https://orcid.org/0000-0001-8436-6074
Adelina Joanita AnatoFrom the ECSTRRA Team (H. Chhoa, S.C., L.B.), Université Paris-Cité, UMR1153, INSERM; Translational Neurovascular Centre (H. Chabriat), GH Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris APHP, Université Paris-Cité and DHU NeuroVasc Sorbonne Paris-Cité; UMR 1161 (H. Chabriat), INSERM; and ENSAI (A.J.A., M.B., F.Z.), Ecole d'ingénieur statistique, data science et big data, Bruz, France.
Mamadou BambaFrom the ECSTRRA Team (H. Chhoa, S.C., L.B.), Université Paris-Cité, UMR1153, INSERM; Translational Neurovascular Centre (H. Chabriat), GH Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris APHP, Université Paris-Cité and DHU NeuroVasc Sorbonne Paris-Cité; UMR 1161 (H. Chabriat), INSERM; and ENSAI (A.J.A., M.B., F.Z.), Ecole d'ingénieur statistique, data science et big data, Bruz, France.
Florent ZittounFrom the ECSTRRA Team (H. Chhoa, S.C., L.B.), Université Paris-Cité, UMR1153, INSERM; Translational Neurovascular Centre (H. Chabriat), GH Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris APHP, Université Paris-Cité and DHU NeuroVasc Sorbonne Paris-Cité; UMR 1161 (H. Chabriat), INSERM; and ENSAI (A.J.A., M.B., F.Z.), Ecole d'ingénieur statistique, data science et big data, Bruz, France.
Sylvie ChevretFrom the ECSTRRA Team (H. Chhoa, S.C., L.B.), Université Paris-Cité, UMR1153, INSERM; Translational Neurovascular Centre (H. Chabriat), GH Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris APHP, Université Paris-Cité and DHU NeuroVasc Sorbonne Paris-Cité; UMR 1161 (H. Chabriat), INSERM; and ENSAI (A.J.A., M.B., F.Z.), Ecole d'ingénieur statistique, data science et big data, Bruz, France.ORCID https://orcid.org/0000-0001-6449-4730
Lucie BiardFrom the ECSTRRA Team (H. Chhoa, S.C., L.B.), Université Paris-Cité, UMR1153, INSERM; Translational Neurovascular Centre (H. Chabriat), GH Saint-Louis-Lariboisière, Assistance Publique des Hôpitaux de Paris APHP, Université Paris-Cité and DHU NeuroVasc Sorbonne Paris-Cité; UMR 1161 (H. Chabriat), INSERM; and ENSAI (A.J.A., M.B., F.Z.), Ecole d'ingénieur statistique, data science et big data, Bruz, France.
Inserm · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objectives: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most frequent hereditary cerebral small vessel disease. It is caused by mutations of the Methods: We used a new synthetic data approach to improve the initial predictive model by incorporating additional genetic information. This method combined the predicted outcomes from the previous model and 5 "synthetic" data sets with the observed outcome in a new data set. We then applied a multiple imputation method for missing data on the mutation location. Results: The new data set included 367 patients who were followed up for 30 to 42 months. In the multivariable model with synthetic data, patients with Discussion: The use of synthetic data improved the predictive model of MDRS change over 3 years in CADASIL. The predictive performance and estimation robustness of the predictive model were enhanced using this approach, whether genetic information was used. A statistically significant association between the location of the mutation in the

Identifiers

PMID38235365
PMCPMC10691224
OpenAlexW4385520241

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.