Gimme My Damn Data (and Let Patients Help!): The #GimmeMyDamnData Manifesto
Bibliographic record
Abstract
Ten years ago, in 2009, "e-Patient Dave" deBronkart delivered an influential keynote speech at the Medicine 2.0 conference in Toronto, organized by the Journal of Medical Internet Research's (JMIR's) editor-in-chief Gunther Eysenbach, who themed the conference around the topics of participation, openness, collaboration, apomediation, and social networking to improve health care for the 21st century-with patient participation being a major component. Many see this as a defining event within the participatory medicine movement, perhaps the beginning of a social movement, similar to the women's rights movement, with the title of Dave's keynote "Gimme my damn data" becoming a rallying cry and hashtag for patients demanding more access to their electronic health records. On the occasion of the 20th anniversary of JMIR (and 10 years after the keynote), we are celebrating the impact of the keynote for the participatory medicine movement and #gimmemydamndata (also #GMDD) by publishing the transcript of these initial conversations as a manifesto of patients' rights to access their data and their right to save their lives.
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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 | 0.031 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.030 | 0.013 |
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".