Evidence map›Paper›PMID 39310240›Full record

ReviewWorld journal of methodology2024

Method "Monte Carlo" in healthcare.

Tsvetelina Velikova, Niya Mileva, Emilia Naseva

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in World journal of methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Format-Preserving Reduction of Canonical Nonlinear Models.Bulletin of mathematical biology · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Observational
  10. 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

3 authors.

Tsvetelina VelikovaMedical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria.
Niya MilevaMedical Faculty, Medical University of Sofia, Sofia 1431, Bulgaria.
Emilia NasevaFaculty of Public Health "Prof. Tsekomir Vodenicharov, MD, Dsc," Medical University of Sofia, Sofia 1431, Bulgaria. e.naseva@foz.mu-sofia.bg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In public health, simulation modeling stands as an invaluable asset, enabling the evaluation of new systems without their physical implementation, experimentation with existing systems without operational adjustments, and testing system limits without real-world repercussions. In simulation modeling, the Monte Carlo method emerges as a powerful yet underutilized tool. Although the Monte Carlo method has not yet gained widespread prominence in healthcare, its technological capabilities hold promise for substantial cost reduction and risk mitigation. In this review article, we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts. We underscore the significance of experiential insights derived from simulated experimentation, especially in resource-constrained scenarios where time, financial constraints, and limited resources necessitate innovative and efficient approaches. As public health faces increasing challenges, incorporating the Monte Carlo method presents an opportunity for enhanced system construction, analysis, and evaluation.

Indexed as

Decision analysisHealthcareHealth economicsModelingMonte CarloSimulationStatistical techniquesStochastic methods

Identifiers

PMID39310240
PMCPMC11230067

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.