Clinical Trial Drug Safety Assessment With Interactive Visual Analytics
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
In this article, we provide guidance on how statisticians can use interactive visual analytics to assist medical personnel through the entire clinical safety review process. For the general assessment of safety data (e.g., adverse events, laboratory measurements), we recommend a review flow with the first step using a display that leverages statistical methods to identify events with stronger evidence of a treatment difference. In combination with clinical knowledge, reviewers can identify events that need further scrutiny. Next, clinical reviewers will be provided with displays that show additional details on these events or patient level information to aid their decision making. For safety topics of interest (e.g., suicidal ideation and behavior), we propose a tailored approach to each topic both at the summary level as well as the patient level. Displays will show only information relevant to the specific topic and for the clinical questions of interest. We also discuss some challenges to fully implement and leverage interactive displays and tools. We encourage broad use of interactive visual analytics in the analysis and display of clinical data.Study sponsor: Eli Lilly and Company.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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