Bibliographic record
Abstract
The development of the health sector around the world is linked to digital technologies, because there is a need to optimize the processes of medical care for the population. Every year there is an increase in this market by a quarter. The use of digital technologies helps to improve health care. The management and organization system implements unified effective systems using the technological capabilities of digitalization in the health care sector. The relevance of the study is related to the need to change the outdated health management system with access to a modern level of technical and administrative support for medical services.The actual tool of digital medicine is cooperative forms of network interaction. We are exploring the further development of digital medicine in the short term, what opportunities can be presented and what results can be obtained by residents. Network communications play an important role in uniting professional medical communities. More than 65% of people between the ages of 21 and 35 have become participants in network technologies, and their number is constantly increasing. Administrative and management staffs are most active in discussions (43%) on the Internet and all respondents have an idea about telemedicine. Patients have the opportunity to get advice by contacting doctors online. The next stage in the development of information technologies is associated with increasing the speed and volume of transmitted data, which will help to predict critical conditions that threaten the patient’s health as quickly as possible. Now there is a discussion about the possibility of not just consulting, but also making diagnoses when patients contact the doctor, discussing in which cases a personal meeting with the doctor is necessary and under what conditions it is enough to provide maximum information about the state of health for diagnosis. Keywords: digital healthcare, digital technologies, adaptation, medical specialists
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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".