The Social Robots Are Coming: Preparing for a New Wave of Virtual Care in Cardiovascular Medicine
Bibliographic record
Abstract
ardiovascular care centers around the world struggle to keep pace with an aging population with complex medical needs, the requirement for physical distancing, and limited hospital resources rerouted to the acute management of COVID-19.The need for innovative, efficient, and efficacious virtual care systems is now more important than ever.Social robots (SRs) may rise to the challenge.SRs are artificial agents physically embodied with human or animal features and imbued with social and emotional intelligence.1 In health care applications, SRs act as a social companion and medical assistant to patients who have limited access to health care or home support services, those who are reticent to onsite hospital visits, or who require frequent follow-ups with health care providers.SRs are equipped with artificial intelligence technology that allows the system to mimic patient-provider encounters, such as recognizing voices, providing eye contact, interpreting and responding appropriately to verbal and nonverbal cues, and adapting to the user's feedback, technology that has been leveraged to help the SR facilitate the detection of important changes in patients' behavior and health status.Today, SRs predominantly supplement gerontological services in patients' and long-term care homes, providing cognitive and social stimulation, supporting medication management, assisting with daily living tasks, and facilitating communication with the appropriate care provider or emergency responder.Recent iterations are now capable of screening for specific health conditions, including alcoholism and prediabetes, while embedded technolo-
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".