PP87 A Descriptive Study Of The Use Of Data Visualization In Full Health Technology Assessment Reports: 2021 To 2023
Bibliographic record
Abstract
Introduction Data visualization is a powerful communication tool to facilitate the understanding of a complex process or data. Data visualization has been used in health technology assessment (HTA) reports for a long period (e.g., PRISMA flow diagrams, critical assessments, or GRADE). This study aims to investigate the number and manners in which HTA reports have been using descriptive data visualization for the years 2021 to 2023. Methods The international HTA database was used to identify and download the HTA reports from the years 2021 to 2023. We applied the database limits: full HTA and completed reports. The records were imported into a Microsoft Excel spreadsheet and screened by eight independent researchers applying the inclusion criteria: full HTA (according to methodological definition), access to the full text, and use of data visualization with a descriptive purpose (we excluded PRISMA flow diagrams, forest plots, and others). The data were observed with the software Power BI. Our analysis included variables such as agency name, country, section, type of visualization, and software. Results From the international HTA database, 1,128 records were exported: 89 records were directly excluded from this set as they were tagged as ongoing or other types of reports in the database; 1,039 records were screened. Around 30 percent of records were included for the data visualization screening criteria after fulfilling our inclusion criteria (full HTA and available full text). Finally, 12 percent of the reports included data visualizations for a descriptive purpose of their results or conclusions. The countries with a higher number of records included in this analysis were Germany (28%), Canada (18%), the UK (16%), and Spain (11%). Conclusions In our sample, we observed that data visualization is not widely used so far to describe outcomes and/or conclusions from HTA reports. An additional finding was the number of records tagged as full HTA in the international HTA database that were excluded as non-full HTA from our study.
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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 | 0.036 | 0.216 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".