ReviewDer Anaesthesist2011
[Real-time feedback systems for improvement of resuscitation quality].
Review in Der Anaesthesist, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Short structured feedback training is equivalent to a mechanical feedback device in two-rescuer BLS: a randomised simulation study.Scandinavian journal of trauma, resuscitation and emergency medicine · 2016Trial
- The influence of real-time feedback on the quality of resuscitation: A prospective study comparing bystanders, paramedic course participants, and emergency physician trainees.GMS journal for medical education · 2025Article
- Detection and Evaluation for High-Quality Cardiopulmonary Resuscitation Based on a Three-Dimensional Motion Capture System: A Feasibility Study.Sensors (Basel, Switzerland) · 2024Article
- Data-driven resuscitation training using pose estimation.Advances in simulation (London, England) · 2023Article
- Monitoring of in-hospital cardiac arrest events with the focus on Automated External Defibrillators--a retrospective observational study.Scandinavian journal of trauma, resuscitation and emergency medicine · 2015Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The quality of chest compression is a determinant of survival after cardiac arrest. Therefore, the European Resuscitation Council (ERC) 2010 guidelines on resuscitation strongly focus on compression quality. Despite its impact on survival, observational studies have shown that chest compression quality is not reached by professional rescue teams. Real-time feedback devices for resuscitation are able to measure chest compression during an ongoing resuscitation attempt through a sternal sensor equipped with a motion and pressure detection system. In addition to the electrocardiograph (ECG) ventilation can be detected by transthoracic impedance monitoring. In cases of quality deviation, such as shallow chest compression depth or hyperventilation, feedback systems produce visual or acoustic alarms. Rescuers can thereby be supported and guided to the requested quality in chest compression and ventilation. Feedback technology is currently available both as a so-called stand-alone device and as an integrated feature in a monitor/defibrillator unit. Multiple studies have demonstrated sustainable enhancement in the education of resuscitation due to the use of real-time feedback technology. There is evidence that real-time feedback for resuscitation combined with training and debriefing strategies can improve both resuscitation quality and patient survival. Chest compression quality is an independent predictor for survival in resuscitation and should therefore be measured and documented in further clinical multicenter trials.
Indexed as
Identifiers
21656065What 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.