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Record W2522021634 · doi:10.1038/npjpcrm.2016.48

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

2016· article· en· W2522021634 on OpenAlexaboutno aff
Liza Cragg, Siân Williams, Thys van der Molen, Mike Thomas, Jaime Correia de Sousa, Niels H. Chavannes

Bibliographic record

Venuenpj Primary Care Respiratory Medicine · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsnot available
FundersNovartis
KeywordsMedicineProtocol (science)Primary careData scienceMedical educationFamily medicineAlternative medicinePathologyComputer science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.593
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0040.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.555
GPT teacher head0.587
Teacher spread0.032 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreProtocol

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
Published2016
Admission routes1
Has abstractyes

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