Fostering the exchange of real-life data across different countries to answer primary care research questions: a protocol for an UNLOCK study from the IPCRG
Bibliographic record
Abstract
This protocol describes a study that will explore the lessons of UNLOCK (Uncovering and Noting Long-term COPD and asthma to enhance Knowledge) over the past 5 years of sharing real-life primary care data from different countries to answer research questions on the diagnosis and management of chronic respiratory diseases. UNLOCK is an international collaboration between primary care researchers and practitioners to coordinate and share data sets of relevant diagnostic and follow-up variables for chronic obstructive pulmonary disease (COPD) and asthma management in primary care. It was set up by members of the International Primary Care Respiratory Group (IPCRG) in response to the identified research need for research in primary care, which recruits patients representative of primary care populations, evaluates interventions realistically delivered within primary care and draws conclusions that will be meaningful to professionals working within primary care. The UNLOCK protocol summary was published in the Primary Care Respiratory Journal in 2010. The primary purpose of UNLOCK is to enable the validation of policy and treatment decisions by using data from unselected primary care populations from diverse contexts in very different countries to evaluate the burden of disease (symptoms, limitations and exacerbations), the natural history of disease, treatment and follow-up and co-morbidities. The unique value of UNLOCK is that data are drawn from primary care databases so there is the potential for longitudinal and cross-sectional studies. It has been 5 years since the UNLOCK collaboration began. In that time its membership has expanded to include 15 countries: Sweden, Spain, Ukraine, Canada, Greece, UK, Netherlands, Norway, Australia, Portugal, Belgium, India, Germany, Uganda and Chile. UNLOCK Group members now offer access to a range of data sets including big data, such as routine healthcare data covering millions of patients, and smaller data sets collected for specific research purposes. Individual members of the UNLOCK Group continue to show they value the collaboration through their active participation in twice-yearly meetings, collaboration on studies and the development of new data sets. However, a range of practical issues have hampered the UNLOCK Group’s ability to translate research ideas into studies published in peer-reviewed publications. These include structural challenges in working on a single study with several researchers from different countries, such as cultural differences; different language competencies and comfort in discussing in English; variations in how COPD and asthma diagnosis and management is reimbursed and incentivised in primary care; the difficulties of participants combining a busy demand-led primary care job with research, which is often unpaid and unsupervised; and differences in primary care research infrastructure and the value/credibility accorded to primary care research. Constraints in working with data sets from different countries and collected for different purposes have also emerged. These include the following: Different ethical and data protection requirements across different countries. The highly variable size of data sets: 100 to >1,000,000 subjects. Differences in data variables and their definitions collected by different countries. Differences in coding systems and practices. The absence of key variables in some data sets. This study will analyse and share learning from the first 5 years of the UNLOCK collaboration.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| 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".