ArticleResuscitation plus2025
Analyzing resuscitation conference content through the lens of the chain of survival.
Article in Resuscitation plus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Resuscitation 2025 congress: launch of the resuscitation guidelines, scientific advances, education, equity, and innovation across the chain of survival.Resuscitation plus · 2026Article
- [Regression analysis to calculate the time point of ROSC-A feasibility study].Die Anaesthesiologie · 2026Article
- Recovery Phase in the Chain of Survival in Cardiac Arrest Patients: Scoping Review.The Malaysian journal of medical sciences : MJMS · 2026Review
- Artificial Intelligence in Cardiopulmonary Resuscitation.Medicina (Kaunas, Lithuania) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Resuscitation science today often focuses on advanced topics such as extracorporeal cardiopulmonary resuscitation or targeted temperature management. However, the specific topics presented at resuscitation conferences have not been thoroughly analyzed. We thus analyzed resuscitation conferences abstracts using a chain of survival framework. Methods: Two major resuscitation conferences (Resuscitation in Greece and Resuscitation Science Symposium in the USA) took place in the fall of 2024. We categorized all abstracts using chain of survival framework, analyzing authors' countries by geography and income. Additionally, artificial intelligence, deep learning, and machine learning approaches for data analysis were examined. Results: "Recognition and prevention" was the top category at both conferences, comprising 37% of topics at Resuscitation 2024 and 32% at Resuscitation Science Symposium 2024. "Early Call for Help", "High-quality Cardiopulmonary Resuscitation", and "Recovery and rehabilitation" were underrepresented, with each <8%. At Resuscitation Science Symposium 2024, "Post-cardiac arrest care" (31%) and "Early defibrillation and advanced life support" (26%) were emphasized, compared to 21% each at Resuscitation 2024 for both chains. Resuscitation 2024 featured participants from 51 countries while Resuscitation Science Symposium 2024 included participants from 19 countries, predominantly high-income ones. At Resuscitation 2024, 54 abstracts, and at Resuscitation Science Symposium 2024, 47 abstracts used machine learning, each with one employing artificial intelligence. None used deep learning. Conclusions: Conference abstracts aligned mainly with the early links of chain of survival and employing machine learning as a data analysis tool. Expanding participation from low-income countries could enhance inclusivity and contribute valuable perspectives to resuscitation science.
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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.