Representation of disorders of the newborn infant by SNOMED CT.
Bibliographic record
Abstract
SNOMED CT is the most sophisticated reference terminology currently available for the representation of healthcare. An unforeseen consequence of the opportunistic evolutionary process for SNOMED CT may be that some terms for disorders of specialised clinical domains are not represented within the terminology. The SNOMED CT July 2006 release was systematically examined using the CliniClue terminology browser to determine whether 434 terms for disorders of the newborn infant are represented within the terminology. There was complete representation for 90.8% of the terms for disorders of the newborn infant, partial representation for 6.4% of the terms, and no representation for 2.8% of the terms. Complete representation is achieved with a single, pre-coordinated SNOMED expression for 96.2% of the terms for disorders of the newborn infant that have complete representation within SNOMED CT. Nearly ninety percent of the SNOMED CT concepts that completely represent these terms have the current Concept Status but less than 40% of these concepts are fully defined SNOMED concepts. Nearly 50% of these SNOMED CT concepts have one or more synonyms. SNOMED CT provides structured representation for the majority of this set of terms that are used for disorders of the newborn infant.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".