Digital Education for Health Care Providers: An Advanced Cardiac Life Support Resource
Bibliographic record
Abstract
Advanced cardiovascular life support (ACLS) refers to a set of urgent treatment guidelines for cardiac emergencies to improve patient outcomes. Strong evidence supports online interactive content in facilitating student learning and engagement. This project introduces an innovative approach to ACLS education, namely a digital ACLS simulation to promote medical resident learning of relevant protocols. No similar resource was found in existing literature, supporting the potential merit of creating this tool for medical education. A scoping review was conducted on current ACLS guidelines to inform interactive content creation with MEDLINE [PubMED] and Web of Science. From this review, a decision tree based on validated care guidelines was derived, guiding digital application development. An online prototype was constructed in HTML, JavaScript, and CSS frameworks. The creation allows users to input cardiovascular emergency patient information and output relevant care instructions based on American Heart Association guidelines. Evidence-based descriptions were provided for each decision, referencing credible external resources for further learning. This interactive application may enhance ACLS protocol learning. Next steps include simulation testing with medical residents to establish whether statistically significant benefits are observed. Long term, this project may inform the development and revision of similar digital resources to enhance medical education delivery.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".