Bibliographic record
Abstract
Background Regular teledermatology services were implemented in Norway in the early 1990s. Based on the available technology at the time, live interactive video consultation systems were implemented to facilitate remote consultations between dermatologists and general practitioners. With the introduction of digital cameras some years later, store-and-forward systems were introduced, but the live video systems remained popular. In the 2000s and early 2010s, there were few changes in the volume of Norwegian teledermatology services. During the 2010s, private teledermatology companies emerged, which provided both store-and-forward and live interactive video consultations. While previous services involved specialists and general practitioners, the new services now offered to patients enable them to interact with dermatologists directly. Objective This lecture aimed to provide a brief overview of the development of telemedicine in Norway before and during the COVID-19 pandemic with special focus on teledermatology. Methods This lecture provides a brief history of telemedicine in Norway with special attention to the impact of the ongoing COVID-19 pandemic. The content is based on personal experiences and literature references. Results The COVID-19 pandemic has had a profound impact on all parts of society. In Norway, it has also affected the way telemedicine is practiced. When the number of new infections increased substantially in early 2020, Norway was under lockdown. This had major consequences on the health care system. In response, the Norwegian government and health authorities strongly encouraged the use of telemedicine and implemented measures to support its use. Since then, there has been a large increase in the number of live video consultations both in specialist and community health care. Conclusions When the necessary technical infrastructure is in place, the remaining barriers to telemedicine use, such as reimbursement and integration of health care systems, can easily be overcome, which would result in high adoption rates of telemedicine. Conflicts of Interest TS is a partner of the Norwegian teledermatology provider “Askin.”
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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) | 0.001 | 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".