Evidence map›Paper›PMID 23140237›Full record

ArticleThe EPMA journal2012

Introduction into PPPM as a new paradigm of public health service: an integrative view.

Tatiana A Bodrova, Dmitry S Kostyushev, Elena N Antonova, Shimon Slavin, Dmitry A Gnatenko, Maria O Bocharova, Michael Legg, Paolo Pozzilli, Mikhail A Paltsev, Sergey V Suchkov

Abstract read
In one paragraph

Article in The EPMA journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Review
  2. Digital paradigm of public health.Frontiers in public health · 2026
    Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Causal relationship between gutThe EPMA journal · 2023
    Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
  14. 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

10 authors.

Tatiana A BodrovaI,M,Sechenov First Moscow State Medical University, Moscow, Russia. dkostushev@gmail.com.
Dmitry S Kostyushev
Elena N Antonova
Shimon Slavin
Dmitry A Gnatenko
Maria O Bocharova
Michael Legg
Paolo Pozzilli
Mikhail A Paltsev
Sergey V Suchkov

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the present state of healthcare, usual medical care is generally given to the already diseased person, while the key link-personal health monitoring underlain by predictive, preventive, and personalised medicine (PPPM) techniques that are being intensively elaborated worldwide-is simply missing. It is this link, based on the recognition of subclinical conditions, prediction, and further preventive measures, that is capable of regulating morbidity and diminishing the rates of disability among able-bodied population, thus significantly cutting the traditionally high costs of treating the already diseased people. To achieve the above-mentioned goal-the elaboration of the PPPM concept and its practical implementation-it is necessary to create a fundamentally new strategy based upon the subclinical recognition of the signs-bioindicators of cryptic abnormalities long before the disease clinically manifests itself. The implementation of PPPM programme requires an adjusted technology for the proper interpretation of diagnostic data, which would allow for the current 'physician-patient' model to be gradually replaced by a novel model, 'medical advisor-healthy men-at-risk'. This is the reason for an additional need in organising combinatorial scientific, clinical, training and educational projects in the area of PPPM to elicit the content of this new branch of medicine.

Identifiers

PMID23140237
PMCPMC3564736

What OpenQuestion holds

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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.