Designing the User Interface of a Nitroglycerin Dose Titration Decision Support System: User-Centered Design Study
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Bibliographic record
Abstract
Abstract Background Nurses adjust intravenous nitroglycerin infusions to provide acute relief for angina by manually increasing or decreasing the dosage. However, titration can pose challenges, as excessively high doses can lead to hypotension, and low doses may result in inadequate pain relief. Clinical decision support systems (CDSSs) that predict changes in blood pressure for nitroglycerin dose adjustments may assist nurses with titration. Objective This study aimed to design a user interface for a CDSS for nitroglycerin dose titration (Nitroglycerin Dose Titration Decision Support System [nitro DSS]). Methods A user-centered design (UCD) approach, consisting of an initial qualitative study with semistructured interviews to identify design specifications for prototype development, was used. This was followed by three iterative rounds of usability testing. Nurses with experience titrating nitroglycerin infusions in coronary care units participated. Results A total of 20 nurses participated, including 7 during the qualitative study and 15 during usability testing (2 nurses participated in both phases). Analysis of the qualitative data revealed four themes for the interface design to be (1) clear and consistent, (2) vigilant, (3) interoperable, and (4) reliable. The major elements of the final prototype included a feature for viewing the predicted and actual blood pressure over time to determine the reliability of the predictions, a drop-down option to report patient side effects, a feature to report reasons for not accepting the prediction, and a visual alert indicating any systolic blood pressure predictions below 90 mm Hg. Nurses' ratings on the questionnaires indicated excellent usability and acceptability of the final nitro DSS prototype. Conclusion This study successfully applied a UCD approach to collaborate with nurses in developing a user interface for the nitro DSS that supports the clinical decision-making of nurses titrating nitroglycerin.
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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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it