[Application Status of Evaluation Methodology of Electronic Medical Record: Evaluation of Bibliometric Analysis].
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In order to provide a reference and theoretical guidance of the evaluation of electronic medical record (EMR) and establishment of evaluation system in China, we applied a bibliometric analysis to assess the application of methodologies used at home and abroad, as well as to summarize the advantages and disadvantages of them. We systematically searched international medical databases of Ovid-MEDLINE, EBSCOhost, EI, EMBASE, PubMed, IEEE, and China's medical databases of CBM and CNKI between Jan. 1997 and Dec. 2012. We also reviewed the reference lists of articles for relevant articles. We selected some qualified papers according to the pre-established inclusion and exclusion criteria, and did information extraction and analysis to the papers. Eventually, 1 736 papers were obtained from online database and other 16 articles from manual retrieval. Thirty-five articles met the inclusion and exclusion criteria and were retrieved and assessed. In the evaluation of EMR, US counted for 54.28% in the leading place, and Canada and Japan stood side by side and ranked second with 8.58%, respectively. For the application of evaluation methodology, Information System Success Model, Technology Acceptance Model (TAM), Innovation Diffusion Model and Cost-Benefit Access Model were widely applied with 25%, 20%, 12.5% and 10%, respectively. In this paper, we summarize our study on the application of methodologies of EMR evaluation, which can provide a reference to EMR evaluation in China.
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.
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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.045 |
| 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) | 0.000 | 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 it