Cardiac Implantable Electronic Device Educational Application for Cardiac Anesthesiology Trainees: Tutorial on App Development
Bibliographic record
Abstract
Unlabelled: Despite the exposure of cardiothoracic anesthesiology trainees to patients with cardiac implantable electronic devices (CIEDs), there is a paucity of formal curricula on this subject. Major impediments to educating cardiothoracic anesthesiology trainees on CIEDs include busy clinical schedules, short staffing, inconsistent trainees' exposure to CIEDs, multiplicity of vendors, and a "millennial" mentality of the new generation of learners. As a result, cardiothoracic anesthesiology trainees graduating from their residency and fellowship programs may lack the competency to manage patients with CIEDs. Herein, we report our systematic approach to designing, validating, mapping, evaluating, and delivering a CIED curriculum on the first mobile app of its kind on this subject. Development of the CIED curriculum proceeded through the Kern 6-step approach of problem identification, determining and prioritizing content, writing goals and objectives, selecting instructional strategies, implementation of the material, and evaluation and applications of lessons learned. This was followed by the delivery of the curriculum in the form of a user-study app and administrator-type app with functionalities in the assessment of the learners' gains, experience, and satisfaction as well as the administrator's capability to update the educational content based on the feedback of the learners and the emerging technology. As such, the CIED app allows asynchronous learning at the pace of the learners and allows, through a multiplicity of educational materials, the ability to digest this complex and understudied subject. We report on the pilot phase of the project. We benefit from the experience of a multidisciplinary team of anesthesiologists, computer scientists, and educators in accomplishing this project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".