Medizinische Ontologien: das Ende des MeSH
Bibliographic record
Abstract
Since the beginning of information technology the complexicity of medical questions and medical information management is an important topic which challenges computer scientists.In the eighties of last century artificial intelligence went awry. Though some core ideas of AI have brought up fruitful results. After all congruent development in a number of different scientific disciplines and the exponential development in computer hardware could meet the high requirements in medical information search. In 2000 Tim Berners-Lee's programmatic request for a Semantic Web gained the ontology topic broader attention.Already 20 years ago NLM started to develop the Unified Medical Language System (UMLS). So in medicine (PubMed) ontology integrated into a semantic net is in operation. Hence it is high time for medical librarians and documentalists to get into this topic although it is covered by a smoke screen of terminology from IT. Ontologies can be understood as tools for classification. So essential contributions from library and documentation science could be expected.This paper should open an entrance to the topic. It will explain fundamental elements of UMLS and includes an annotated list of literature for further studies.
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 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 | 0.053 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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; both teacher heads agree on what is shown here.
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".