A New Standards-based Grammar for Linking Aggregate Datasets
Bibliographic record
Abstract
The theme of this session is the linking and cross-referencing of disparate aggregate datasets that need to be combined for pruporses of reporting and/or analysis. The session leverages, as a global case study, the US Government's President's Emergency Plan for AIDS Relief (PEPFAR) programme. PEPFAR is a $7 billion per year programme supporting the delivery of HIV-related services, medicines, and commodities in 58 low and middle-income countries (www.pepfar.gov). PEPFAR has an immense datastore of monitoring, evaluation and reporting (MER) indicators that have been collected from all its supported countries over the course of its 15 years of operations.
 The goal of the session is to describe for attendees a newly-developed, standards-based grammar for describing interoperable aggregate data exchange and the message schemas needed to support it. The session facilitators are the primary authors of this new standard. Using the PEPFAR case study as a working example, the session explores how disparate HIV data elements and indicators from PEPFAR-supported countries are cross-referenced to each other and collected into a single central datastore to support analysis, management and reporting across the global programme. The specific HIV example will be elaborated upon to illustrate generalizable techniques that can be applied to linking aggregate datasets in other use cases (e.g. reporting to the annual WHO global health observatory, multiple provinces reporting to a federal health data institute, etc.).
 The session will be facilitated by Xenophon Santas and James Kariuki of the US CDC, Bob Jolliffe of the University of Oslo's Health Information Systems Programme (HISP) and Derek Ritz of ecGroup Inc (a Canadian health informatics consultancy). All four facilitators are members of the Quality, Research and Publich Health (QRPH) technical committee of the international digital health standards body, Integrating the Healthcare Enterprise (IHE; www.ihe.net). The session's content and examples will leverage the facilitators' first-hand experience working on HIV-related projects in low and middle-income countries (e.g. South Africa, Rwanda, Kenya, Malawi, Zimbabwe, Uganda, Sierre Leone, Vietnam, the Philippines and elsewhere).
 It is intended that the session will be conducted using an interactive workshop style. Attendees who wish it will have an opportunity to engage in participative (hands-on) learning. To get started, information will be provided about the standards-based grammar and how it works. Then, results from the facilitators' efforts leveraging this method to link multiple disparate HIV-related datasets will be presented. As a hands-on activity, attendees who have notebook computers will be able to connect to an open source software solution (www.dhis2.org) and "play in a sandbox" to try for themselves some of the techniques that have been described.
 As learning objectives, it is expected that attendees will:
 
 Be introduced to data linking use cases outside of their everyday experience
 Learn about a new technique for expressing aggregate content schema that supports interoperable data exhange
 Apply new skills in a hands-on, worked example.
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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.007 | 0.001 |
| 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".