Voice-based screening for SARS-CoV-2 exposure in cardiovascular clinics
Why this work is in the frame
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Bibliographic record
Abstract
Aims: Artificial intelligence (A.I) driven voice-based assistants may facilitate data capture in clinical care and trials; however, the feasibility and accuracy of using such devices in a healthcare environment are unknown. We explored the feasibility of using the Amazon Alexa ('Alexa') A.I. voice-assistant to screen for risk factors or symptoms relating to SARS-CoV-2 exposure in quaternary care cardiovascular clinics. Methods and results: We enrolled participants to be screened for signs and symptoms of SARS-CoV-2 exposure by a healthcare provider and then subsequently by the Alexa. Our primary outcome was interrater reliability of Alexa to healthcare provider screening using Cohen's Kappa statistic. Participants rated the Alexa in a post-study survey (scale of 1 to 5 with 5 reflecting strongly agree). This study was approved by the McGill University Health Centre ethics board. We prospectively enrolled 215 participants. The mean age was 46 years [17.7 years standard deviation (SD)], 55% were female, and 31% were French speakers (others were English). In total, 645 screening questions were delivered by Alexa. The Alexa mis-identified one response. The simple and weighted Cohen's kappa statistic between Alexa and healthcare provider screening was 0.989 [95% confidence interval (CI) 0.982-0.997] and 0.992 (955 CI 0.985-0.999), respectively. The participants gave an overall mean rating of 4.4 (out of 5, 0.9 SD). Conclusion: Our study demonstrates the feasibility of an A.I. driven multilingual voice-based assistant to collect data in the context of SARS-CoV-2 exposure screening. Future studies integrating such devices in cardiovascular healthcare delivery and clinical trials are warranted. Registration: https://clinicaltrials.gov/ct2/show/NCT04508972.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 it