Determining the test characteristics of claims‐based diagnostic codes for the diagnosis of venous thromboembolism in a medical service claims database
Bibliographic record
Abstract
PURPOSE: To determine the test characteristics of diagnostic codes within a medical service claims database for deep vein thrombosis (DVT) and pulmonary embolism (PE). METHODS: The Regie de l' Assurance Maladie du Québec (RAMQ) administers the health insurance program in Québec, Canada. RAMQ claims data were obtained for subjects with objectively diagnosed DVT with or without PE who were participants in the Venous Thrombosis Outcomes (VETO) Study from April 2001 to July 2002. Using the date of DVT and PE diagnosis in the VETO record as the reference standard, the proportion of subjects correctly classified by RAMQ diagnostic codes was determined for the exact date of DVT and PE occurrence and for four expanded time windows around this date. RESULTS: In all, 355 VETO patients were included, 301 with DVT alone and 54 with DVT and PE. Overall, 97% of VETO cases had a RAMQ diagnostic code for DVT and 82% of VETO cases with PE had a RAMQ diagnostic code for PE. Sensitivity for DVT and PE was 52% (95% confidence interval (CI), 47-57) and 35% (95% CI, 23-49), respectively for the exact date of diagnosis, and 87% (95% CI, 83-90) and 78% (95% CI, 64-88), respectively for a 60-day window around this date. As all VETO participants had DVT, specificity for the diagnosis of DVT could not be determined. CONCLUSION: Diagnostic codes within a medical service claims database are relatively sensitive indicators for DVT and PE, and use of claims data for VTE research purposes can be considered.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".