Development of a Concept Dictionary to Standardize Definitions and Classifications While Working With a Common Repository of Linked Administrative Data
Bibliographic record
Abstract
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".