Tools for categorization of diagnostic codes in hospital data: Operationalizing CCSR into a patient data repository
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
Abstract Background The Clinical Classification Software refined version (CCSR) is a tool to aggregate International Classification of Diseases, 10th Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS) diagnosis codes into clinically meaningful categories. ICD-10-CM/PCS codes are primarily used in the United States and the tool has not been optimized for use with other country-specific ICD-10 coding systems. Method We developed an automated procedure for mapping Canadian ICD-10 codes (ICD-10-CA) to CCSR categories using discharge diagnosis data from adult medical hospitalizations at 7 hospitals between Apr 1 2010 and Dec 31 2020, and manually validated the results. Results There were 383,972 Canadian hospital admissions with 5,186 distinct ICD-10 discharge diagnosis codes. Only 46.6% of ICD-10-CA codes could be mapped directly to CCSR categories. Our algorithm improved mapping of hospital codes to CCSR categories to 98.2%. Validation of the algorithm demonstrated a high degree of accuracy with strong interrater agreement (observed proportionate agreement of 0.98). The algorithm was critical for mapping the majority of diagnosis codes associated with heart failure (96.6%), neurocognitive disorders (96.0%), skin and subcutaneous tissue infections (97.2%), and epilepsy (92.5%). Conclusion Our algorithm for operationalizing CCSR into a patient data repository ( https://github.com/GEMINI-Medicine/gemini-ccsr ) has been validated for use with Canadian ICD-10 codes and may be useful to clinicians and researchers from diverse geographic locations.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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