The Potential for Remotely Mentored Patient-Performed Home Self-Monitoring for New Onset Alveolar-Interstitial Lung Disease
Bibliographic record
Abstract
Purpose: Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is an acute respiratory illness. Although most infected persons are asymptomatic or have only mild symptoms, some patients progress to devastating disease; such progression is difficult to predict or identify in a timely manner. COVID-19 patients who do not require hospitalization can self-isolate at home. Calls from one disease epicenter identify the need for homebased isolation with telemedicine surveillance to monitor for impending deterioration. Methodology: Although the dominant approach for these asymptomatic/paucisymptomatic patients is to monitor oxygen saturation, we suggest additionally considering the potential merits and utility of home-based imaging. Chest computed tomography is clearly impractical, but ultrasound has shown comparable sensitivity for lung involvement, with major advantages of short and simple procedures, low cost, and excellent repeatability. Thoracic ultrasound may thus allow remotely identifying the development of pneumonitis at an early stage of illness and potentially averting the risk of insidious deterioration to severe pneumonia and critical illness while in home isolation. Conclusions: Lung sonography can be easily performed by motivated nonmedical caregivers when directed and supervised in real time by experts. Remote mentors could thus efficiently monitor, counsel, and triage multiple home-based patients from their “control center.” Authors believe that this approach deserves further attention and study to reduce delays and failures in timely hospitalization of home-isolated patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".