Acceptability and Utility of a Web-Based Patient-Completed Clinical Decision Aid for the Differential Diagnosis of Transient Loss of Consciousness: Qualitative Interview Study
Bibliographic record
Abstract
Background: Web-based patient-completed clinical decision aids (CDAs) have the potential to reduce inefficient resource use and patient risk in acute and emergency settings while minimizing additional clinician time burdens. However, such interventions must be acceptable for use by their target audience-patients. Objective: The objective of this study is to assess acceptability and utility to patients of a novel online patient-completed CDA for the differential diagnosis of transient loss of consciousness (TLoC). Methods: Within a larger validation study of a patient-completed CDA, we conducted nested qualitative semistructured interviews with a purposive sample of 20 patients who used the CDA in the study and performed thematic analysis of interview transcripts. Results: We identified 11 themes within the data: 3 addressing the content of the CDA, 3 addressing the online implementation, and 4 addressing usability and acceptability of the CDA. Respondents generally felt an online CDA was easy to complete and acceptable, though they felt that increased options to personalize descriptions of their experience would be helpful and offered guidance on how to make it a more useful resource for patients as well as clinicians. We present good practice points for the design of patient-completed online CDAs on the basis of our thematic analysis. Conclusions: Findings suggest that patient-completed CDAs may be accessible and feasible in acute and emergency settings, though further research is needed to explore their real-world usability. In designing such tools, clinicians should endeavor to maintain their accessibility for all relevant patient groups and to use them to provide direct patient benefit, as well as to support clinical decision-making, for example, through simultaneous patient-directed outputs.
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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 | 0.046 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".