Applying an ICD-10 to ICD-11 mapping tool to identify causes of death codes in an Alberta dataset
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
BACKGROUND: The most recent and 11th revision of the International Classification of Disease (ICD-11) is in use as of January 2022, and countries around the globe are now preparing for the implementation of ICD-11 and transition from the 10th revision (ICD-10). Translation of current coding is required for historical comparisons. METHODS: We applied the World Health Organization (WHO) mapping tables to current Centers for Disease Control and Prevention (CDC) Lists of ICD-10 coding of underlying causes of death to assess what ICD-11 codes look like in an Alberta sample of causes of death (COD). We prepared frequency tables for a single year of COD in Alberta based on the CDC grouping of COD. RESULTS: The mapping success rate at the ICD-10 code level for the adult population (> 18 years) was 96.6% and 100% for children (1-17 years) and infants (< 1 year). The mapping success rate by patient was 99.5% for the adult population patient deaths and 100% for children and infants. We mapped ICD-11 codes to identify the ten most frequently reported underlying COD in Alberta for 24,645 deaths in adults, children, and infants in 2017. CONCLUSIONS: Apart from two codes, all ICD-10 codes could be mapped to ICD-11 for underlying COD. These findings suggest that the ability to translate from the two iterations of coding will be feasible for future applications of health services data.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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