The validity of ICD codes coupled with imaging procedure codes for identifying acute venous thromboembolism using administrative data
Bibliographic record
Abstract
The purpose of this study was to evaluate the accuracy of using a combination of International Classification of Diseases (ICD) diagnostic codes and imaging procedure codes for identifying deep vein thrombosis (DVT) and pulmonary embolism (PE) within administrative databases. Information from the Alberta Health (AH) inpatients and ambulatory care administrative databases in Alberta, Canada was obtained for subjects with a documented imaging study result performed at a large teaching hospital in Alberta to exclude venous thromboembolism (VTE) between 2000 and 2010. In 1361 randomly-selected patients, the proportion of patients correctly classified by AH administrative data, using both ICD diagnostic codes and procedure codes, was determined for DVT and PE using diagnoses documented in patient charts as the gold standard. Of the 1361 patients, 712 had suspected PE and 649 had suspected DVT. The sensitivities for identifying patients with PE or DVT using administrative data were 74.83% (95% confidence interval [CI]: 67.01-81.62) and 75.24% (95% CI: 65.86-83.14), respectively. The specificities for PE or DVT were 91.86% (95% CI: 89.29-93.98) and 95.77% (95% CI: 93.72-97.30), respectively. In conclusion, when coupled with relevant imaging codes, VTE diagnostic codes obtained from administrative data provide a relatively sensitive and very specific method to ascertain acute VTE.
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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 | 0.021 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".