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Record W2890999860 · doi:10.23889/ijpds.v3i4.680

Development of a Concept Dictionary to Standardize Definitions and Classifications While Working With a Common Repository of Linked Administrative Data

2018· article· en· W2890999860 on OpenAlexaff
Erin M. Macdonald, Anna Chu, Michelle Cooper, Ruth Croxford, Raquel Duchen, Kinwah Fung, Sima Gandhi, Jodi M. Gatley, Fiona Jin, Wayne Khuu, Alex Kopp, Laura C. Maclagan, Joan Porter, Robert Stumpo, Ali Syed Zaidi, Mahmoud Azimaee

Bibliographic record

VenueInternational Journal for Population Data Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsInstitute for Clinical Evaluative Sciences
Fundersnot available
KeywordsComputer scienceConsistency (knowledge bases)Variety (cybernetics)Quality (philosophy)Data scienceKey (lock)Information retrievalWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

IntroductionSupporting standardized approaches to common tasks is an important component of quality research using linked administrative data. Standard concept definitions and classifications are vital for ensuring accuracy and consistency in definitions between projects, and improving efficiency and quality. Other leading organizations have published online standard definitions of concepts and classifications. Objectives and ApproachWe developed a comprehensive concept dictionary using a standardized definition template of key components including data sources, codes, scale or range of values, validation details, limitations, SAS code and formats, related concepts, and MeSH terms. A web-based application (built on the Microsoft SharePoint platform) was developed to offer the latest web content authoring capabilities, and advanced search mechanisms enabling the user to search concepts by MeSH terms and key words. It also allowed for navigating concepts through category navigation including clickable categories and sub-categories. Entries will be reviewed annually to ensure the content remains up-to-date. ResultsTo date, ten concepts, with accompanying codes, have been published on the concept dictionary with another ten currently undergoing editorial review. These concepts span a variety of topics such as injuries, mental health and addictions-related outpatient services, and annual physical exams. New concepts written by content experts and reviewed by an editorial committee will be added on an on-going basis; thirty concepts are currently under development. Conclusion/ImplicationsDevelopment of a concept dictionary provides standardized definitions, algorithms and codes to ensure consistency and quality of research and analysis across multiple projects. Future aims include expansion of the internal organizational site to an external site through collaboration with key stakeholders.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.070
metaresearch head score (Gemma)0.111
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.070
Threshold uncertainty score0.370

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0700.111
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0180.014
Science and technology studies0.0040.006
Scholarly communication0.0150.024
Open science0.0060.009
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0150.013

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 head0.271
GPT teacher head0.415
Teacher spread0.144 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

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Citations1
Published2018
Admission routes1
Has abstractyes

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