Bibliographic record
Abstract
Abstract This session highlights the experience from two countries in data linkage: Canada and the UK. Canada's ten provinces and three territories have primary responsibility for the organization and delivery of health care services and for the supervision of health care providers. As such, many data linkage initiatives are at the provincial level, and the recent Health Data Research Network aims to facilitate access to multi- province data for researchers. Also the Social Data Linkage Environment (SDLE) represents a national effort led by the federal government (Statistics Canada). The SDLE has a centrally maintained ‘key register' containing new identifiers based on a number of identifying characteristics which are used to link a wide range of data sets, including the Labour Force Survey, national health surveys, the hospital discharge database, census data, and a range of provincial data on crime, education, mental health, etc. Thus, the SDLE is not a consolidated database per se, but a tool to link other databases, and has potential for use in scientific research. Similar to Canada, the different nations of the UK operate separate data linkage initiatives. One internationally renowned national database, the Clinical Research Practice Datalink (CPRD), reflects the important role of GPs. The CPRD brings together de-identified microdata data from GP practices in the UK on the basis of the respective electronic practice information systems. The data collected includes demographic details, information on service providers, clinical presentation, referrals, vaccinations, laboratory findings and medication prescriptions, with the ability to link to other data sets to research the care pathway. As of 2019, the CPRD data covers 42 million patients and has been used for more than 2000 scientific publications that have researched issues such as drug safety, drug use, the effectiveness of health policy measures, the organization of service delivery and the development of risk factors.
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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 | 0.271 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.033 |
| Science and technology studies | 0.032 | 0.015 |
| Scholarly communication | 0.045 | 0.020 |
| Open science | 0.011 | 0.053 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".