The TRANSFoRm project: Experience and lessons learned regarding functional and interoperability requirements to support primary care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: The current model of medical knowledge production, transfer, and application suffers from serious shortcomings. Learning health systems (LHS) have recently emerged as a potential solution-systems in which health information generated from patients is continuously analyzed to improve knowledge that will be transferred to patient care. METHOD: Various approaches of data integration already exist and could be considered for the implementation of a LHS. We discuss what are the possible informatics approaches to address the functional requirements of LHS, in the specific context of primary care, and present the experience and lessons learned from the TRANSFoRm project. RESULT: Implemented in 4 countries around 5 systems, TRANSFoRm is based on a local-as-view data mediation approach integrating the structural and terminological models in the same framework. It clearly demonstrated that it has the potential to address the requirements for a LHS in primary care, by dealing with data fragmented across multiple points of service. Also, it has the potential to support the generation of hypotheses from the context of clinical care, retrospective and prospective research, and decision support systems that improve the relevance of medical decisions. CONCLUSION: The LHS approach embodies a shift from an institution-centered to a patient-centered perspective in knowledge production and transfer and can address important challenges in the primary care setting.
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it