Development and evaluation of a multimedia e-learning resource for electrolyte and acid-base disorders
Bibliographic record
Abstract
This article reports on the development and evaluation of a Web-based application that provides instruction and hands-on practice in managing electrolyte and acid-base disorders. Our teaching approach, which focuses on concepts rather than details, encourages quantitative analysis and a logical problem-solving approach. Identifying any dangers to the patient is a vital first step. Concepts such as an "appropriate response" to a given perturbation and the need for electroneutrality in body fluids are used repeatedly. Our Electrolyte Workshop was developed using Flash and followed an iterative design process. Two case-based tutorials were built in this first phase, with one tutorial including an interactive treatment simulation. Users select from a menu of therapies and see the impact of their choices on the patient. Appropriate text messages are displayed, and changes in body compartment sizes, brain size, and plasma sodium concentrations are illustrated via Flash animation. Challenges encountered included a shortage of skilled Flash developers, budgetary constraints, and challenges in communication between the authors and the developers. The application was evaluated via user testing by residents and specialists in internal medicine. Satisfaction was measured with a questionnaire based on the System Usability Scale. The mean System Usability Scale score was 78.4 ± 13.8, indicating a good level of usability. Participants rated the content as being scientifically sound; they liked the teaching approach and felt that concepts were conveyed clearly. They indicated that the application held their interest, that it increased their understanding of hyponatremia, and that they would recommend this learning resource to others.
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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".