Comparing ICD-Data Across Countries: A Case for Visualization?
Bibliographic record
Abstract
We present our preliminary results of an international survey on the practical adoption and use of the International Classification of Diseases (ICD) from a visualization and visual analytics perspective. The ICD system, in different versions, is globally used for coding morbidity and mortality statistics, however, coding practices vary across countries. Our survey includes questions about hospital data collection systems, use of features in ICD, and training of ICD coding specialists. Variations in ICD could hinder comparability and limit generalizability of observed findings. Our preliminary results establish the current state of ICD use and training internationally, and will ultimately be valuable to the World Health Organization to further research on how to improve ICD coding, and enhance international comparisons of health data. From a visualization and visual analytics perspective, the current differences in adoption and use of ICD poses challenges and opportunities. For example, when morbidity-data from two countries differ in their coding, can we still compare data from these countries, and if so, then under which circumstances? We discuss how visualization and visual analytics might help in these situations.
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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 | 0.187 | 0.537 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.026 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| 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".