The Future of Telemedicine in O&G
Bibliographic record
Abstract
Abstract This paper presents an ongoing work in on the future of telemedicine in O&G. There has been a huge development in the use of video consultation between remote patients and the doctors. We believe the future of telemedicine in O&G will add to this workflow by investigating how we can transfer visual medical data between "offshore nurses" and "medical experts" at hospitals onshore in order to improve diagnostics and treatment. We will describe a decision support system that supports an optimal workflow and collaboration, between medics onshore and offshore. The goal is to make better and faster medical decisions, and improve the quality of healthcare offshore. The oil companies have much of the same structure and same challenges in remote medical treatment. We investigate an optimal workflow including how technology supports a new telemedicine work process by transmitting very high quality information (e.g. ultrasound images) to the cardiovascular medical experts. We will review our work on developing a prototype "on the go" solution between medics offshore and the medical experts onshore at the hospital. The concept will be based on a Pad/PC solution capturing the ultrasound image transmission between the user and experts, a systematic work process and a knowledge base integrated in the Pad/PC "on the go solution". With optimal workflow it should not take more than 5-7 minutes from the starting point to have a decision from the medical expert. This will improve diagnostics, medical safety and health quality on offshore installations.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".