Thromboelastometry (<scp>ROTEM</scp>) Assessing Hypercoagulability in Patients Referred for Thrombophilia Screening
Bibliographic record
Abstract
INTRODUCTION: Thrombophilia, a blood coagulation disorder, poses risks of venous thromboembolism (VTE). Coagulation assays may not be sufficient to assess VTE risk and global assays such as Rotational Thromboelastometry (ROTEM) may add valuable information. We investigated ROTEM's capacity to detect hypercoagulability in patients undergoing thrombophilia screening, its potential impact on patient outcomes, and limitations. METHODS: Comprehensive clinical, laboratory, genetic tests, and ROTEM (EXTEM and INTEM) were conducted for 356 patients referred for thrombophilia screening at an academic hospital outpatient unit. Hypercoagulability was identified as a shorter clot formation time (CFT), larger alpha angle (AA), and greater maximum clot firmness (MCF), and was compared in patients with and without VTE. Statistically this was analyzed using Mann-Whitney U and Chi-square tests with p < 0.05 considered significant. RESULTS: Among 356 patients, 64.6% had previous VTE, with 76.9% experiencing one event, 14.3% recurrent (35.6% unprovoked, 64.4% provoked). 22.5% of patients were on anticoagulation. Those with VTE history exhibited significant alterations in EXTEM and INTEM parameters compared to those without (p < 0.001), showing decreased CFT and increased AA and MCF. However, receiver operating characteristic curves for these variables indicated that none were able to discriminate between those individuals with and without thromboembolic complications. CONCLUSION: ROTEM does not appear to be a strong discriminatory test. However, it can detect hypercoagulopathy in patients referred for thrombophilia screening. Abnormal ROTEM may indicate a higher risk for recurrence. However, this can only be determined in prospective cohort studies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".