The International Patient Summary Standard and the Extensibility Requirement
Bibliographic record
Abstract
The International Patient Summary Standard (EN 17269) normalizes the dataset within the European Guideline on cross-border exchange of a patient summary. This dataset has been widely appreciated and been taken as the basis for projects in both Europe and wider afield, e.g. U.S.A, Canada and more. The dataset is a relatively mature dataset and it is currently in its third iteration (i.e., 2013, 2016, 2020). Even so, to move from a policy-driven guideline to a formal standard was not straight forward. The paper describes how the 'minimal and non-exhaustive' dataset could be the basis for a reference standard; one that was intended to facilitate both an 'implementable' and 'sustainable' solution. In particular, the requirement of 'extensibility' for the standard dataset had to be addressed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.138 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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 source (direct Gemma or distilled Codex), 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".