3D Remote Monitoring and Diagnosis during a Pandemic: Holoportation and Digital Twin Requirements
Bibliographic record
Abstract
COVID-19 regulations presented clinicians with a new set of challenges that affected their ability to efficiently provide patient care and, as a result, telemedicine was rapidly adopted to deliver care remotely. However, these telemedicine platforms undermine patient care due to clinicians' inability to acquire all the relevant patient information required to diagnose and treat the patient. To explore this gap, we conducted a requirements analysis for the development of a 3D remote patient monitoring and diagnosis platform, by using a user-centric design methodology. In this requirements analysis, we elicited information about the clinical domain, identified clinicians’ requirements, elicited clinicians’ insights, and documented the clinicians' requirements. The outcome was the emergence of refined clinicians' requirements to guide the implementation of the Digital Twin concept paired with holoportation for remote 3D monitoring and diagnosis of patients. We anticipate that the application of a 3D telemedicine platform with these requirements for patient care during a pandemic could potentially enhance clinicians' efficiency and the effectiveness of remote patient care.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".