Open data webinar: Revolutionizing clinical research - Exploring open data initiatives for innovation and impact
Bibliographic record
Abstract
"Revolutionizing clinical research: Exploring open data initiatives for innovation and impact" an open data webinar hosted by the Pediatric Sepsis Data CoLaboratory. Given how pervasive (and complex) Artificial Intelligence (AI) I is going to be, it is unlikely that a few organizations can truly regulate the technology. Its oversight will require everyone pitching in. That, in turn, requires everyone having some basic understanding of how AI is developed, and more importantly, the risks associated with its use. But how do we build capacity in a field that is moving at warped speed? There will be no individual experts in the field, only collective wisdom. Legacy education and knowledge systems are too inflexible and siloed to keep up with the science and understand the sociology of AI. Datathons are designed to operationalize the multi-disciplinary hive learning that addresses the challenges of AI education and training. In this webinar, speakers discuss data bias and AI and their experience with optimizing reuse of data to advance health research through Datathons. Presenters: 1. "What's the Fuss about Artificial Intelligence (AI)?" - Speaker: Leo Anthony Celi, Principal Research Scientist, Massachusetts Institute of Technology; Associate Professor of Medicine, Harvard Medical School; Editor-in-Chief, PLOS Digital Health. 2. "SATI-Q Program: 20 Years of History in Quality Benchmarking, Evolution and Future Perspectives" - Speaker: Ariel Leonardo Fernández, Software Developer, Quality Benchmarking Program (SATI-Q), Argentina Society of Intensive Care (SATI). Data Description: Presentation slides, webinar video - Full (55m), webinar video - Celi (21m), webinar video - Fernández (13m).
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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.104 | 0.027 |
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".