A CONTROLLED CLINICAL STUDY TO EVALUATE A PROPRIETARY NON-INVASIVESMARTPHONE BASED DIGITAL BIOMARKER TOOL LYFAS® IN ENABLING EARLYDETECTION OF COVID-19 INFECTION AMONG ASYMPTOMATIC INDIVIDUALS
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19), is a pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the increasing number of individuals infected with COVID-19, there is a growing need for easy, dependable, accurate, scalable, and cost-effective tools. These tools should serve the purpose of population screening/surveillance, diagnosis, and prognosis. Unlike the other pandemics in the past, the current viral infection presents itself with long incubation period up to 14days. This situation is challenging because it results in an extremely high proportion of asymptomatic individuals (up to 80% and more). These individuals in turn contribute to high risk of transmission among the vulnerable population with coexisting diseases and the aged. Currently, the real time PCR test and serology is being largely employed only in symptomatic individuals. Therefore, there is a need for a dependable additional test to identify the asymptomatic individuals, to qualify them for the conrmatory rtPCR and/or serology tests. To meet these the above requirement, we decided to repurpose and test our proprietary non-invasive smartphone-based health screening mobile application Lyfas. The validated parameters captured from Lyfas were found to be inherent indicators of several cardiorespiratory, cardiovascular, autonomic nervous system, hematology and biochemistry anomalies. Apparently, these anomalies were also found and reported as early indicators of COVID-19 infection. Therefore, by means of rationale selection of these parameters, we derived a unique LYFAS_COVID_SCORE to enable detection and prioritize conrmatory testing for asymptomatic individuals. A controlled clinical study was conducted in 25(n=25) subjects to prove the hypothesis and establish the difference in LYFAS_COVID_SCORE between an infected and non-infected group of individuals. The LYFAS_COVID_SCORE derived out of this clinical study was found to be different between the two groups. The positive infected test group had a signicantly high(p=.00012) median score of 19.8(range 9.9 to 22.1) compared to 6.9(range 2.2 to 7.8) that of the healthy uninfected control group. The accuracy, sensitivity and the specicity of the study was found to be 92%, 93.2% and 90% respectively. The result of this study is therefore an evidence to use Lyfas as a potential tool in the identication, screening, and surveillance of asymptomatic individuals. The ndings from the study deserves to be extended to a large population conrmatory clinical study to add proof and value to the tool.
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How this classification was reachedexpand
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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".