Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to: (1) explore and analyze the structure and content of 19 SNOMED Clinical Terms (SNOMED CT) national editions; (2) identify overlaps and types of quality issues; and (3) offer recommendations to improve the quality of these national editions. MATERIALS AND METHODS: We used expression constraint language and structured query language to analyze and compare the national editions hosted on the SNOMED International SNOMED CT Browser and the International and Canadian Edition Release Format Two files. RESULTS: The 19 national editions authored over 275 000 concepts and over 2.1 million descriptions. Modules were used to organize drug extensions, language translations, patient-friendly descriptions, maps, and subsets. The national editions included 27 languages and dialects reference sets, maps to international, national, and local terminologies, and over 1100 subsets. Since 2012, over 28 000 extensions have been promoted to the International Edition. Overlaps were also identified between national editions. DISCUSSION: Challenges of extensions included inconsistent modeling of concepts and quality issues, versioning and maintenance, and risks to semantic interoperability and data analysis. We suggest improved functionality in authoring tools to identify overlapping content across national editions and the incorporation of auditing methods to ensure high-quality extensions, increased collaboration between countries, and the accelerated harmonization of extensions into the International Edition. CONCLUSION: Nineteen countries have developed over 2.4 million extension concepts and descriptions, and it is important to harmonize the national editions through ongoing collaboration to maintain the integrity and consistency of SNOMED CT as a global reference terminology.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".