IMECCHI-DATANETWORK: empowering knowledge generation through international data network
Bibliographic record
Abstract
ABSTRACT
 ObjectivesThe International Methodology Consortium for Coded Health Information (IMECCHI), an international collaboration of health services researchers, launched the IMECCHI-DATANETWORK initiative in October 2015. Its main objective is to enable replication of observational studies across countries through a distributed data infrastructure.
 ApproachIn a distributed data infrastructure, individual raw data are not shared. Instead, data are converted locally using a common data model (CDM) and loaded into a common software for data processing and analysis. Whenever a study protocol is agreed upon and ethically approved, an ad hoc procedure is programmed - using that software - including the data processing steps needed to create the analytical dataset from the CDM: record linkage, case selection, sampling, matching, etc. The procedure is then shared and locally run by each partner to generate an analytical dataset of integrated data. Analytical datasets may then be shared and pooled for statistical analyses.
 ResultsSix partners of the IMECCHI collaboration, located in countries across 4 continents (Canada, Denmark, Italy, New Zealand, South Korea, and Switzerland), currently participate in the initiative. They first conducted a survey to describe the origin, content, completeness and main attributes of each table in their original databases. Based on the results of the survey, a CDM was created, encompassing 4 tables of coded or structured data to be linked at the individual level using a common personal identifier: (1) characteristics of the subjects with dates of birth and death; (2) hospital discharge summaries with diagnosis and procedure codes, and admission, discharge and procedure dates; (3) drug dispensing information with date of dispensing, drug name, duration of the amount of active principle according to the Defined Daily Dose of the World Health Organization; (4) causes of death. In each table, additional attributes describe the coding systems in which the other attributes are coded. Using such specific attributes facilitates interoperability across multiple coding systems. The open source Java-based software, TheMatrix, which operates on flat csv files using a domain-specific programming language, was chosen to embed the ad hoc procedure.
 ConclusionWithin the IMECCHI-DATANETWORK initiative, databases from various countries will be locally converted in a CDM which will facilitate study replication in a distributed fashion while granting interoperability across coding systems. Through such international data networks, data are empowered for creating results which are generalizable to multiple countries. Cross-border data sharing and international comparisons are also facilitated.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.008 | 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".