Does de-identification require consent under the GDPR and English common law?
Bibliographic record
Abstract
Data de-identification has many benefits in the context of the General Data Protection Regulation (GDPR). One of the recurring questions is whether consent is required to anonymise or de-identify data. In this paper, the authors make the case that no consent is required for anonymisation or other forms of de-identification under the GDPR, although additional conditions have to be met where special category data is anonymised. Further, under the English equitable duty of confidentiality, consent is generally not required if the de-identification is performed by the direct care team or on behalf of the direct care team; it is arguable that de-identification can also be performed by others outside of the direct care team, but less clear. The alternative would be special authorisation under section 251 of the National Health Service (NHS) Act.
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 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 | 0.174 | 0.331 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.024 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".