Bibliographic record
Abstract
BACKGROUND: Telemedical Maritime Assistance Service (TMAS) is one of the fundamental components of medical assistance delivery at sea. However, while onshore telemedicine is undergoing a fast growth, these research and clinical investments unfortunately did not yet benefit for telemedicine at sea. DIVERGENCES BETWEEN TELEMEDICINE AT SEA AND ONSHORE: While telemedicine aims at providing distant health care, telemedicine at sea and onshore bear major differences, particularly for merchant vessels, and to a lesser extent for passenger vessels, which can be divided between structural differences, differences of practices, and policy differences. CONVERGENCES BETWEEN TELEMEDICINE AT SEA AND ONSHORE: Despite the existence of important divergences between telemedicine at sea and telemedicine onshore, these two major branches of distant health care delivery still converge in some respects. CONCLUSIONS: Identifying the convergences between telemedicine at sea and telemedicine onshore might contribute to increase and optimise the transfer from research on onshore telemedicine to maritime telemedicine, and to overcome the relatively low amount of research performed on telemedicine at sea compared to its onshore counterpart.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".