ReviewInternational endodontic journal2026
From Virtual Modelling to Smarter Endodontic Innovation: In Silico Research for Translational Decision Making.
Review in International endodontic journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Pulp calcification and pulp canal obliteration: from biological mechanisms to contemporary clinical management and digital navigation.The Saudi dental journal · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo discuss how in silico research may function as an early translational decision layer in endodontics by supporting the prioritisation of hypotheses, technologies and experimental conditions before biological and clinical validation. SUMMARY: Endodontic outcomes emerge from interacting anatomical, mechanical, chemical, microbial and host-related variables. Conventional studies using extracted teeth, artificial canals, selected microbial models and standardised tests remain essential, yet cannot represent the full variability encountered in clinical settings. In silico approaches, including finite element analysis, computational fluid dynamics, probabilistic simulations, molecular modelling and predictive toxicology, may extend preclinical investigation by mapping plausible scenarios across pathological pathways, instrument performance, biomaterials and irrigation. Their contribution depends on a clearly defined context of use, justified input data, transparent assumptions, verification, validation, sensitivity analysis and uncertainty assessment. Computational outputs should be interpreted according to the question the model was designed to address and should guide subsequent experimental selection rather than be treated as independent evidence of clinical effectiveness.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.