A systematic review of validated methods for identifying venous thromboembolism using administrative and claims data
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (VTE) is a serious complication. Large claims databases can potentially identify the effects that medications have on VTE. The purpose of this study is to evaluate the evidence supporting the validity of VTE codes. METHODS: A search of MEDLINE database is supplemented by manual searches of bibliographies of key relevant articles. We selected all studies in which a claim code was validated against a medical record. We reported the positive predictive value (PPV) for the VTE claim compared to the medical record. RESULTS: Our search strategy yielded 345 studies, of which only 19 met our eligibility criteria. All of the studies reported on ICD-9 codes, but only two studies reported on pharmacy codes, and one study reported on procedure codes. The highest PPV (65%-95%) was reported for the combined use of ICD-9 codes 415 (pulmonary embolism), 451, and 453 (deep vein thrombosis) as a VTE event. If a specific event like DVT (PPV 24%-92%) or PE (PPV 31%-97%) was evaluated, the PPV was lower than when the combined events were examined. Studies that included patients after orthopedic surgery reported the highest PPV (96%-100%). CONCLUSIONS: The use of ICD-9 415, 451, and 453 are appropriate for the identification of VTE in claims databases. The codes performed best when codes were evaluated in patients at higher risk of 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 | 0.040 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.039 | 0.030 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".