MétaCan
Menu
Back to cohort
Record W131864321

Representation of disorders of the newborn infant by SNOMED CT.

2008· article· en· W131864321 on OpenAlexaff
Andrew James, Kent A. Spackman

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsSickKids FoundationUniversity of Toronto
Fundersnot available
KeywordsSNOMED CTTerminologyRepresentation (politics)Artificial intelligenceComputer scienceSystematized Nomenclature of MedicineNatural language processingSet (abstract data type)Knowledge representation and reasoningMedicineLinguisticsProgramming language
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.134

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.230
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2008
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicBiomedical Text Mining and OntologiesFrench-language works237,207