Validity of the <i>International Classification of Diseases 10th revision</i> code for hyperkalaemia in elderly patients at presentation to an emergency department and at hospital admission
Bibliographic record
Abstract
Objectives Evaluate the validity of the International Classification of Diseases, 10th revision (ICD-10) code for hyperkalaemia (E87.5) in two settings: at presentation to an emergency department and at hospital admission. Design Population-based validation study. Setting 12 hospitals in Southwestern Ontario, Canada, from 2003 to 2010. Participants Elderly patients with serum potassium values at presentation to an emergency department (n=64 579) and at hospital admission (n=64 497). Primary outcome Sensitivity, specificity, positive-predictive value and negative-predictive value. Serum potassium values in patients with and without a hyperkalaemia code (code positive and code negative, respectively). Results The sensitivity of the best-performing ICD-10 coding algorithm for hyperkalaemia (defined by serum potassium >5.5 mmol/l) was 14.1% (95% CI 12.5% to 15.9%) at presentation to an emergency department and 14.6% (95% CI 13.3% to 16.1%) at hospital admission. Both specificities were greater than 99%. In the two settings, the positive-predictive values were 83.2% (95% CI 78.4% to 87.1%) and 62.0% (95% CI 57.9% to 66.0%), while the negative-predictive values were 97.8% (95% CI 97.6% to 97.9%) and 96.9% (95% CI 96.8% to 97.1%). In patients who were code positive for hyperkalaemia, median (IQR) serum potassium values were 6.1 (5.7 to 6.8) mmol/l at presentation to an emergency department and 6.0 (5.1 to 6.7) mmol/l at hospital admission. For code-negative patients median (IQR) serum potassium values were 4.0 (3.7 to 4.4) mmol/l and 4.1 (3.8 to 4.5) mmol/l in each of the two settings, respectively. Conclusions Patients with hospital encounters who were ICD-10 E87.5 hyperkalaemia code positive and negative had distinct higher and lower serum potassium values, respectively. However, due to very low sensitivity, the incidence of hyperkalaemia is underestimated.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".