Development of a decision aid for cardiopulmonary resuscitation and invasive mechanical ventilation in the intensive care unit employing user-centered design and a wiki platform for rapid prototyping
Bibliographic record
Abstract
BACKGROUND: Upon admission to an intensive care unit (ICU), all patients should discuss their goals of care and express their wishes concerning life-sustaining interventions (e.g., cardiopulmonary resuscitation (CPR)). Without such discussions, interventions that prolong life at the cost of decreasing its quality may be used without appropriate guidance from patients. OBJECTIVES: To adapt an existing decision aid about CPR to create a wiki-based decision aid individually adapted to each patient's risk factors; and to document the use of a wiki platform for this purpose. METHODS: We conducted three weeks of ethnographic observation in our ICU to observe intensivists and patients discussing goals of care and to identify their needs regarding decision making. We interviewed intensivists individually. Then we conducted three rounds of rapid prototyping involving 15 patients and 11 health professionals. We recorded and analyzed all discussions, interviews and comments, and collected sociodemographic data. Using a wiki, a website that allows multiple users to contribute or edit content, we adapted the decision aid accordingly and added the Good Outcome Following Attempted Resuscitation (GO-FAR) prediction rule calculator. RESULTS: We added discussion of invasive mechanical ventilation. The final decision aid comprises values clarification, risks and benefits of CPR and invasive mechanical ventilation, statistics about CPR, and a synthesis section. We added the GO-FAR prediction calculator as an online adjunct to the decision aid. Although three rounds of rapid prototyping simplified the information in the decision aid, 60% (n = 3/5) of the patients involved in the last cycle still did not understand its purpose. CONCLUSIONS: Wikis and user-centered design can be used to adapt decision aids to users' needs and local contexts. Our wiki platform allows other centers to adapt our tools, reducing duplication and accelerating scale-up. Physicians need training in shared decision making skills about goals of care and in using the decision aid. A video version of the decision aid could clarify its purpose.
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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".