A survey on technologies used during Out of Hospital Cardiac Arrest
Bibliographic record
Abstract
Background: Out of hospital cardiac arrest (OHCA) causes close to 400,000 deaths every year in North America, and it is also a leading cause of death among young athletes.OHCA is a treatable medical condition, and the patient's survival chances can be increased if immediate treatment is provided to the patient.However, non-treatment of the patient leads to a dramatic decline in survival chances at 10% per minute.Currently, various technologies are being used, and many more are being researched to reduce the time to provide early treatment to the patient.Objective: This survey focuses on summarizing various available technologies for use during OHCA.This survey focuses on evaluating technologies used in each step of the OHCA process.Methods: In this survey, articles were searched using the term "ohca" on Google Scholar and more than 18,000 articles were found.The articles were further filtered using keywords for each stage of the OHCA process (2,128).For each step, articles were filtered again using author developed method to select articles relevant to each technology for each step of the OHCA process (339).Finally, articles were manually filtered by authors based on their relevance and 112 articles were used in this survey.Results: The technologies that exist today work independently and are not linked with the other steps of the OHCA process.Integration between these technologies could help in reducing time and increase the survival chances of the patient.Also, if it is found that some of the proposed solutions are experimental in nature and not meant to be used in the real-world OHCA scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".