Synchronized tag clouds for exploring semi-structured clinical trial data
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
Searching and comparing information from semi-structured repositories is an important, but cognitively complex activity for internet users. The typical web interface displays a list of results as a textual list which is limited in helping the user compare or gain an overview of the results from a series of iterative queries. In this paper, we propose a new interactive, lightweight technique that uses multiple synchronized tag clouds to support iterative visual analysis and filtering of query results. Although tag clouds are frequently available in web interfaces, they are typically used for providing an overview of key terms in a set of results, but thus far have not been used for presenting semi-structured information to support iterative queries. We evaluated our proposed design in a user study that presents typical search and comparison scenarios to users trying to understand heterogeneous clinical trials from a leading repository of scientific information. The study gave us valuable insights regarding the challenges that semi-structured data collections pose, and indicated that our design may ease cognitively demanding browsing activities of semi-structured information.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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