Evidence map›Paper›PMID 31999716›Full record

ArticlePloS one2020

Characterization of near death experiences using text mining analyses: A preliminary study.

Vanessa Charland-Verville, Demetrius Ribeiro de Paula, Charlotte Martial, Helena Cassol, Georgios Antonopoulos, Blaine Alexander Chronik, Andrea Soddu, Steven Laureys

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

5 citing papers in PubMed, 19 citations in OpenAlex.

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  5. Review
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

8 authors at 2 institutions in 2 countries.

Vanessa Charland-VervilleGIGA Consciousness, Coma Science Group, University and University Hospital of Liège, Liège, Belgium.ORCID 0000-0001-7641-6916
Demetrius Ribeiro de PaulaGIGA Consciousness, Coma Science Group, University and University Hospital of Liège, Liège, Belgium.
Charlotte MartialGIGA Consciousness, Coma Science Group, University and University Hospital of Liège, Liège, Belgium.
Helena CassolGIGA Consciousness, Coma Science Group, University and University Hospital of Liège, Liège, Belgium.
Georgios AntonopoulosGIGA Consciousness, Coma Science Group, University and University Hospital of Liège, Liège, Belgium.
Blaine Alexander ChronikBrain and Mind Institute, University of Western Ontario, London, Ontario, Canada.
Andrea SodduBrain and Mind Institute, University of Western Ontario, London, Ontario, Canada.
Steven LaureysGIGA Consciousness, Coma Science Group, University and University Hospital of Liège, Liège, Belgium.
University of Liège · BEWestern University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The notion that death represents a passing to an afterlife, where we are reunited with loved ones and live eternally in a utopian paradise, is common in the reports of people who have encountered a "Near-Death Experience" (NDE). NDEs are thoroughly portrayed by the media but empirical studies are rather recent. The definition of the phenomenon as well as the identification of NDE experiencers is still a matter of debate. To date, NDEs' identification and description in studies have mostly derived from answered items in questionnaires. However, questionnaires' content could be restricting and subject to personal interpretation. We believe that in addition to their use, user-independent statistical text examination of freely expressed NDEs narratives is of prior importance to help capture the phenomenology of such a subjective and complex phenomenon. Towards that aim, we included 158 participants with a firsthand retrospective narrative of their self-reported NDE that we analyzed using an automated text-mining method. The output revealed the top words expressed by experiencers. In a second step, a hierarchical clustering analysis was conducted to visualize the relationships between these words. It revealed three main clusters of features: visual perceptions, emotions and spatial components. We believe the user-independent and data-driven text mining approach used in this study is promising by contributing to the building a rigorous description and definition of NDEs.

Indexed as

Data MiningDeathLife Change EventsSelf ReportHumans

Identifiers

PMID31999716
PMCPMC6992169
OpenAlexW3003514983

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.