Validity of the International Classification of Diseases, 10th Revision codes for lithium toxicity in adult patients at hospital admission: a cohort study in Canada
Bibliographic record
Abstract
OBJECTIVE: (ICD-10) healthcare database diagnosis codes for lithium toxicity at hospital admission in Ontario, Canada. DESIGN: Population-based retrospective validation study. SETTING: A total of 152 hospitals linked to a provincial laboratory database in Ontario, Canada, from 2007 to 2023. PARTICIPANTS: Patients 50 years of age or older taking lithium with hospital-based serum lithium laboratory measurements during admission to the hospital (n=2804). OUTCOME MEASURES: Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) comparing an ICD-10 diagnostic coding algorithm for lithium toxicity to a serum lithium concentration of 1.5 mmol/L or more. The codes used in the algorithm were T568, T435, Y495, X41 and X49. Serum lithium values and changes in the concentration of serum lithium from baseline levels in patients with and without a diagnosis code for lithium toxicity (code-positive and code-negative, respectively). RESULTS: The sensitivity of the ICD-10 coding algorithm for identifying a serum lithium level≥1.5 mmol/L was 84% (95% CI 81% to 87%). The specificity and the NPV were over 88%, and the PPV was 63% (95% CI 60% to 66%). The median (IQR) serum lithium measurement in code-positive patients was 1.7 (1.2 to 2.2) mmol/L, and it was 0.6 (0.4 to 0.9) mmol/L in code-negative patients. The median (IQR) increase in serum lithium concentration compared with the most recent prehospital baseline values was 0.7 (0.2 to 1.3) mmol/L in code-positive patients and 0.0 (-0.2 to 0.2) mmol/L in code-negative patients. CONCLUSION: In Ontario, the sensitivity of the ICD-10 coding algorithms was moderate for identifying a serum lithium level≥1.5 mmol/L at hospital admission. The presence or absence of the ICD-10 codes for lithium toxicity at hospital admission successfully differentiated two groups of patients with distinct serum lithium measurements.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".