Telemedicine Booths for Screening Cardiovascular Risk Factors: Prospective Multicenter Study
Bibliographic record
Abstract
Background: Cardiovascular risk factors such as hypertension often remain undetected and untreated. This was particularly problematic during the COVID-19 pandemic when there were fewer in-person medical consultations. Objective: This study aimed to determine whether health screening using a telemedicine booth would have an impact on people's medical care during the COVID-19 pandemic. Methods: Health screening was run using a telemedicine booth (the consult station) that was placed in three different vaccination centers in northern France between July 2021 and September 2021. Participants followed a series of instructions to obtain various measures, including their blood pressure, BMI, and heart rate. If any measures were found to be outside of the normal range, participants were advised to consult a doctor. After 3 months, the participants with abnormal readings were contacted by telephone and were asked a series of standardized questions. The primary outcome was the percentage of respondents who reported that they had consulted a doctor since the health check. Results: Approximately 6000 people attended the 3 vaccination centers over the study period. Of these, around 2500 used the consult station. A total of 1333 participants (53.3%) were found to have abnormal readings, which mostly concerned their blood pressure, heart rate, or BMI. There were 638 participants who responded to the follow-up call, and 234 of these (37%) reported that they had consulted a doctor since the health check. However, 158 of the 638 respondents (24.8%) reported that they would have consulted a doctor even without the screening. Conclusions: We succeeded in screening large numbers of people for cardiovascular risk factors during the COVID-19 pandemic by using a telemedicine booth. Although relatively few follow-up call respondents reported that they went on to consult a physician, the screening would nevertheless have raised people's awareness of their cardiovascular risk factors.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".