Electronic Health
Bibliographic record
Abstract
Computing and networking techniques and technologies are gradually penetrating into every aspect of health care and medical practice.Many believe that important benefits can be achieved with the marriage of these two areas, including the delivery of high-quality health care at lower cost.Electronic health (e-Health) has become a very important area of focus and activity in multiple domains, such as health promotion, health care and maintenance, public health, medical science, health service, data management, image processing, telecommunication, wireless network, and operational research.The purpose of the special issue is to provide a high quality exposure to advances and experiences in e-Health related topics and to stir interest in advancing e-Health to the next level.The call for papers for this special issue attracted numerous exciting responses from the research community.Submissions covered diverse areas of interests.Unfortunately, space limitations allowed only nine papers to be selected for inclusion in this special issue, after extensive peer review.In the following paragraphs, we briefly introduce the nine accepted papers.Health information technology plays an important role in supporting decision making, health care delivery, and management of health services.Many sociotechnical factors
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.301 | 0.134 |
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".