Bibliographic record
Abstract
The demand for high quality, safe and quantity for healthcare is increasing while the resources remain unchanged. Adoption of better information technology can achieve significant improvements in quality and safety of healthcare delivery in the environment of increasing pressure on healthcare systems. This will also contribute to contain healthcare cost in the long run. Many developed and developing countries in the world pay attention on appropriate use of information communication technology(ICT) in healthcare domain. Some countries such as the US, UK, Australia, Canada and others adopted strategic plan of National Health Information Infrastructure for next 10 years. The objectives for ICT application of developed countries are summarized as: - To improve access to clinical records; - To reduce clinical errors and improve safety of patients; - To improve access to quality information on health for patients and healthcare professionals; - To improve efficiency of healthcare processes; and - To contain healthcare costs. The core of the ICT adoption in health is to have universal availability of electronic health and clinical records(EHR) at the point of care. This review, therefore, briefly described the definition, architectures, essential functionalities and applicable standards of EHR.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.023 | 0.027 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".