Coding of Stroke and Stroke Risk Factors Using <i>International Classification of Diseases</i> , Revisions 9 and 10
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Surveillance is necessary to understand and meet the future demands stroke will place on health care. Administrative data are the most accessible data source for stroke surveillance in Canada. The International Classification of Diseases, 10th revision (ICD-10) coding system has potential improvements over ICD-9 for stroke classification. Our purpose was to compare hospital discharge abstract coding using ICD-9 and ICD-10 for stroke and its risk factors. METHODS: We took advantage of a switch in coding systems from ICD-9 to ICD-10 to independently review stroke patient charts. From time periods April 2000 to March 2001, 717 charts, and from April 2002 to March 2003, 249 charts were randomly selected for review. Using a before-and-after time period design, the accuracy of hospital coding of stroke (part I) and stroke risk factors (part II) using ICD-9 and ICD-10 was compared. We used careful definitions of stroke and its types based on ICD-9 using the fourth and fifth digit modifier codes. RESULTS: Stroke coding was equally good with ICD-9 (90% [CI95 86 to 93] correct) and ICD-10 [92% (CI95 88 to 95 correct) with ICD-10. There were some differences in coding by stroke type, notably with transient ischemic attack, but these differences were not statistically significant. Atrial fibrillation, coronary artery disease/ischemic heart disease, diabetes mellitus, and hypertension were coded with high sensitivity (81% to 91%) and specificity (83% to 100%). ICD-10 was as good as ICD-9 for stroke risk factor coding. CONCLUSIONS: Passive surveillance using administrative data are a useful tool for identifying stroke and its risk factors using both ICD-9 and ICD-10.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".