Evidence map›Paper›PMID 39537728›Full record

ArticleScientific reports2024

The PERMIT guidelines for designing and implementing all stages of personalised medicine research.

Paula Garcia, Rita Banzi, Vibeke Fosse, Chiara Gerardi, Enrico Glaab, Josep Maria Haro, Emanuela Oldoni, Raphaël Porcher, Judit Subirana-Mirete, Cecilia Superchi and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. 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

11 authors.

Paula GarciaEuropean Clinical Research Infrastructure Network (ECRIN), Paris, France. garcialobato.paula@gmail.com.
Rita BanziCenter for Health Regulatory Policies, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Vibeke FosseCenter for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
Chiara GerardiCenter for Health Regulatory Policies, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Enrico GlaabCentre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
Josep Maria HaroResearch and Development Unit, Parc Sanitari Sant Joan de Déu, Barcelona, 08830, Spain.
Emanuela OldoniEATRIS ERIC, European Infrastructure for Translational Medicine, Amsterdam, The Netherlands.
Raphaël PorcherUniversité Paris Cité, Centre de Recherche Épidémiologie et Statistiques (CRESS- UMR1153), INSERM, INRAE, Paris, France.
Judit Subirana-MireteResearch and Development Unit, Parc Sanitari Sant Joan de Déu, Barcelona, 08830, Spain.
Cecilia SuperchiUniversité Paris Cité, Centre de Recherche Épidémiologie et Statistiques (CRESS- UMR1153), INSERM, INRAE, Paris, France.
Jacques DemotesEuropean Clinical Research Infrastructure Network (ECRIN), Paris, France.

Funding

Horizon 2020 Framework Programme 874825
6 · The paper itself

Abstract

Personalised medicine (PM) research programmes represent the modern paradigm of complex cross-disciplinary research, integrating innovative methodologies and technologies. Methodological research is required to ensure that these programmes generate robust and reproducible evidence. The PERMIT project developed methodological recommendations for each stage of the PM research pipeline. A common methodology was applied to develop the recommendations in collaboration with relevant stakeholders. Each stage was addressed by a dedicated working group, specializing in the subject matter. A series of scoping reviews that mapped the methods used in PM research and a gap analysis were followed by working sessions and workshops where field experts analyzed the gaps and developed recommendations. Through collaborative writing and consensus building exercises, the final recommendations were defined. They provide guidance for the design, implementation and evaluation of PM research, from patient and omics data collection and sample size calculation to the selection of the most appropriate stratification approach, including machine learning modeling, the development and application of reliable preclinical models, and the selection and implementation of the most appropriate clinical trial design. The dissemination and implementation of these recommendations by all stakeholders can improve the quality of PM research, enhance the robustness of evidence, and improve patient care.

Indexed as

Precision MedicineBiomedical ResearchGuidelines as TopicHumansMachine LearningResearch Design

Identifiers

PMID39537728
PMCPMC11560950

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 Socratic graph.