Medicininių duomenų apsikeitimo HL7 standarte metodai ir jų taikymas
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]
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.057 |
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".