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Record W2892297510 · doi:10.23889/ijpds.v3i4.709

A National Concept Dictionary

2018· article· en· W2892297510 on OpenAlexaffabout
Mahmoud Azimaee, J. Charles Victor, Marian J. Vermeulen, Mark Smith

Bibliographic record

VenueInternational Journal for Population Data Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsManitoba HealthInstitute for Clinical Evaluative Sciences
Fundersnot available
KeywordsComputer scienceFlexibility (engineering)Coding (social sciences)PopulationWorld Wide WebData scienceInformation retrieval

Abstract

fetched live from OpenAlex

Overall objectives or goalMost of the organizations that use population administrative data for research purposes have internal repository of validated definitions and algorithms of their own. Many of these concepts and definitions are applicable or at least adaptable to other organizations and jurisdictions. A comprehensive National (and potentially International) Concept Dictionary could help investigators to carry out methodologically sound work using consistent and validated algorithms using a shared pool of knowledge and resources. The Institute for Clinical Evaluative Sciences (ICES) in Ontario, Canada has recently modernized its internal Concept Dictionary by adopting standard templates based on the Manitoba Centre for Health Policy (MCHP) Concept Dictionary, reviewing and updating existing content and tagging the concept entries with appropriate MeSH terms and data sources, and adding standard computer code (e.g., SAS coding) where appropriate. A SharePoint® web-based application has been developed to provide advanced tagging, searching and browsing features. We envision a wiki-based Concept Dictionary hosted on a cloud-based environment with very granular access controls to provide enough flexibility for each participating organization to control their own content. This means each organization will be able to decide on how to share their own concepts (or part of them) with the public or internal users. All content will be tagged with MeSH terms and as well with the organization’s name that initially posts each entry. Other organizations which find the same concept applicable to their own use can tag the same entry with their organization name or refer to a secondary adapted entry if adaptation to fit their data and methodologies is required. The Search feature will allow refining the search criteria by MeSH terms, data sources, and also organization/jurisdiction name. Multiple layers of access controls will allow each organization to have their own groups of users with different standard privileges such as Local Administrators, Authors and Approvers (or Publishers). The Approver (Publisher) users within each organization can publish each entry for internal or public view. This way, for example, a definition/algorithm can be viewable only within the organization until the validation process is complete, and then the entry can be made publically available, while some sections, such as computer code, can remain restricted to the organization. We will discuss challenges in developing and maintaining such a platform including the costs, governance, intellectual property rights, copyrights and liabilities for the participating organizations. The intended output or outcomeWe aim to use this opportunity to form a working group from the interested organizations that are ready to participate and commit in developing this collaborative platform. After the conference, there will be follow up sessions with the members of the working group to plan and develop the online application.

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)0.011
metaresearch head score (Gemma)0.028
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: none
Teacher disagreement score0.096
Threshold uncertainty score0.320

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.012
Science and technology studies0.0050.003
Scholarly communication0.0100.015
Open science0.0040.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0960.069

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 head0.070
GPT teacher head0.424
Teacher spread0.353 · 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".

Quick stats

Citations1
Published2018
Admission routes2
Has abstractyes

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