Administrative data ICD-10 diagnostic codes identifies most lab-confirmed SARS-CoV-2 admissions but misses many discharged from the Emergency Department
Bibliographic record
Abstract
We estimated the operating characteristics of ICD-10 code U07.1, introduced by the World Health Organization in 2020, to identify lab-confirmed SARS-CoV-2. CCEDRRN is a national research registry of adults (March 2020-August 2021) with suspected/confirmed SARS-CoV-2 identified in Canadian emergency departments (EDs) using chart review (symptoms, clinical information, and lab test results including SARS-CoV-2 polymerase chain reaction, PCR results). CCEDRRN data were linked to administrative hospitalization discharge and ED ICD-10 diagnostic codes (accessed centrally via the Canadian Institute for Health Information). We identified ICD-10 diagnostic codes in CCEDRRN participants. We defined lab-confirmed SARS-CoV-2 based on at least one positive PCR in the 0-14 days before the ED presentation and/or during hospitalization (in those admitted from ED). We performed separate analyses for CCEDRRN participants discharged from ED and those hospitalized from the ED. Additional analyses were stratified by province, sex, age, and (for hospitalized patients) timing of the first PCR test. The sensitivity of ICD-10 code U07.1 for a positive SARS-CoV-2 test was 93.6% (95% CI 93.0-94.1%) in those hospitalized from ED and 83.0% (95% CI 82.1-83.9%) in those discharged from the ED. Sensitivity was similar across provinces and demographics, but in each stratified analysis, values were higher in those hospitalized versus those discharged from ED. The ICD-10 diagnostic code for U07.1 within administrative data identified most lab-confirmed SARS-CoV-2 within persons hospitalized from ED, although a significant number of cases discharged from ED were missed. This should be considered when using administrative data for research and public health planning.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".