Novel Interactive Data Visualization
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The ESCAPE (Endovascular Treatment for Small Core and Anterior Circulation Proximal Occlusion With Emphasis on Minimizing CT to Recanalization Times) randomized clinical trial collected a large diverse data set. However, it is difficult to fully understand the effects of the study on certain patient groups and disease progression. We developed and evaluated an interactive visualization of the ESCAPE trial data. METHODS: We iteratively designed an interactive visualization using Python's Bokeh software library. The design was evaluated through a user study, which quantitatively evaluated its efficiency and accuracy against traditional modified Rankin Scalegraphic. Qualitative feedback was also evaluated. RESULTS: The novel interactive visualization of the ESCAPE data are publicly available at http://escapevisualization.herokuapp.com/. There was no difference in the efficiency and accuracy when comparing the use of the novel with the traditional visualization. However, users preferred the novel visualization because it allowed for greater exploration. Some insights obtained through exploration of the ESCAPE data are presented. CONCLUSIONS: Novel interactive visualizations can be applied to acute stroke trial data to allow for greater exploration of the results. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT01778335.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 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".