Bibliographic record
Abstract
Background Since the beginning of 2020, many societal systems have been used to extend the health care system, which were not planned for, and as such, there is concern for its collapse. Clearly, the collapse of the health care system, primarily hospitals, has been a key concern, and many initiatives, including lockdown and curfew, were taken to avoid such a collapse. The internet was the key platform used to enable people to work from home, provide remote teaching, conduct meetings on the web, etc. However, when it comes to data communication and processing, the risk of collapse is not the only risk, and maybe not even the biggest one. Many systems were not properly adapted for used in such a hurry, which did not allow time (and concern) for a proper risk and privacy assessment. Objective This paper presents internet performance statistics and analyzes how this knowledge can be used in future designs of internet-based telemedical solutions. Methods Statistics regarding traffic increases and security attacks on the internet during 2020 and 2021 were analyzed. Results The internet did not collapse during the COVID pandemic—as many people had predicted. However, the massive use of the internet, in new innovative ways, created a number of new opportunities for cybersecurity breach. Especially, the use videoconferences enabled made-in-middle attacks, phishing, and other classical breaches in new ways due to insufficient authentication and content encryption. Conclusions Even though a large amount of experience has been gathered with respect to scaling eHealth systems, a minimum amount of improvement with respect to privacy and security has been identified. Conflicts of Interest None declared.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".