A systematic review of validated methods for identifying venous thromboembolism using administrative and claims data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 it