Is ischaemia-modified albumin a test for venous thromboembolism?
Bibliographic record
Abstract
OBJECTIVE: Patients with symptoms of deep vein thrombosis (DVT) and pulmonary embolism (PE) commonly present to the emergency department (ED). The aim of this study was to assess the role of ischaemia-modified albumin (IMA) testing in the diagnosis of venous thromboembolism (VTE). METHODS: This was a prospective diagnostic cohort study. Inpatients and ED patients >16 years of age investigated for PE or DVT at a single hospital were eligible for study consent. Blinded IMA analysis was performed on the first blood sample taken from each patient. Patients underwent reference standard investigation for PE or DVT, including 3-month follow-up. Receiver operating characteristic (ROC) curves were constructed for IMA and the IMA:albumin ratio in the diagnosis of all VTE, PE and DVT. A sensitivity analysis was performed. RESULTS: 452 patients were consented and investigated for DVT, and 354 patients were consented and investigated for PE (806 in total). 348 patients investigated for PE had IMA testing as did 195 of the first 199 DVT patients. VTE prevalence was 19.7%. The IMA:albumin ratio performed better than IMA alone. The area under the ROC curve (AUC) for IMA:albumin in all VTE was 0.60 (95% CI 0.54 to 0.66), in DVT 0.56 (95% CI 0.46 to 0.65) and in PE 0.63 (95% CI 0.56 to 0.71). In ED patients with symptoms of PE, the AUC for IMA:albumin was 0.69 (95% CI 0.60 to 0.78). CONCLUSIONS: IMA testing cannot be used alone to diagnose DVT or PE, although there is a moderate association with PE in ED patients.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".