Medicininių duomenų apsikeitimo HL7 standarte metodai ir jų taikymas
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
Medical data exchange between medicine institutions is very important subject. In\nLithuania at this time hasn’t installed united medical system which allows doctors to check\npatient’s case-history from all hospitals. For example abroad, in Canada for example has united\nmedical system in all country hospitals. Canada hospitals has a lot of different medical data store\nsystems installed, and to exchange data between them, they need to accept one united standard,\nwhich allows to get and perceive accepted data in all the country. They accepted to use HL7\nstandard for medical data exchange. I will try to research, can we use Canada practice in Lithuania,\nsome data and other’s research. Our object to create HL7 system which will send HL7 message\nanswers to HL7 message queries. All queries and answers must follow the requirements of HL7\nstandard. We will use KMU Heart center database which is in operation for data capture. The fact\nthat database is in operation, adds additional data analysing. Analyzing involves how data met, the\nHL7 requirements and there they must be put in HL7 message. The data coding in HL7 message\nis defined in HL7 standard, so this part is clear. But the data exchange and events processing part\nlets user to take his own decisions. In the analytical part of our work we will try to touch questions\nabout data capture from database and coding it to HL7 message. Also we will touch questions\nabout data exchange methods, what tools or solutions must be used to... [to full text]
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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