D-PATH (Data Privacy Assessment Tool For Health) for Biomedical Data Sharing
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
The Data Privacy Assessment Tool for Health (D-PATH) is a proof-of-concept online tool designed to help users intending to share biomedical data identify applicable legal obligations and relevant best practices. D-PATH provides a series of simple questions to assess important aspects of the data sharing task, such as the user’s legal jurisdiction and the types of entities involved. Based on the combination of answers that the user provides, D-PATH will generate a list of privacy obligations and security-best practices, categorized into themes of 1) accountability, 2) lawfulness of storage, transfer, and protection, and 3) security and safeguards that will likely apply in the user’s scenario. Currently, the D-PATH focuses on Canadian and European privacy laws and various global best-practice policies, but there are plans to extend this in later iterations of the tool. D-PATH was developed specifically to inform users about their legal privacy obligations and best practices and was written to facilitate compliant and ethical data sharing. As a proof-of-concept, D-PATH demonstrates the potential value of a tool in simplifying and translating complex concepts into more accessible formats. Such a tool can be adapted and valuable in many different contexts, such as training core researchers in data sharing laws and practices.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.102 | 0.191 |
| 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