Bibliographic record
Abstract
In early 2016, the International Committee of Medical Journal Editors (ICMJE) proposed that responsible sharing of de-identified individual-level data be required for clinical trials published in their affiliated journals. There would be a delay in implementing this policy to allow for the necessary informed consents to work their way through ethical review. Meanwhile, some researchers and policy makers have conflated the notions of de-identification and anonymity. The former is a process that seeks to mitigate disclosure risk though careful application of rules and statistical analysis, while the latter is an absolute state. The consequence of confusing the process and the state is profound. Extensions to the ICMJE proposal based on the presumed anonymity of data include: sharing unconsented data; sharing data without managing access, as Open Data; and proposals to sell data. This essay aims to show that anonymity (the state) cannot be guaranteed by de-identification (the process), and so these extensions to the ICMJE proposal should be rejected on governance grounds, if no other. This is not as negative a position as it might seem, as other disciplines have been aware of these limitations and concomitant responsibilities for many years. The essay concludes with an example from social science of managed access strategies that could be adopted by the medical field.
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 | 0.108 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.096 |
| Scholarly communication | 0.026 | 0.037 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.024 | 0.037 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".