Clinical utility of thrombin generation using ST-Genesia® in patients with hereditary and acquired thrombophilia: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The role of thrombin generation (TG) in the setting of thrombophilia testing remains unclear. Hence, we aimed to investigate the diagnostic utility of TG with ST-Genesia® instrument to discriminate between patients with and without different thrombophilias. METHODS: We conducted a single-center cross-sectional study of all non-anticoagulated patients who underwent conventional thrombophilia testing for factor V Leiden, prothrombin gene G20210A mutation (PTM), protein C, S and antithrombin deficiency (PCD, PSD, ATD), and antiphospholipid antibody syndrome (APS) because of previous venous thromboembolism (VTE), unexplained arterial thrombosis or a positive family history for VTE. To assess the diagnostic utility of TG, we calculated the area under the receiver operating curve (AUC), thresholds for 85 %, 95 % and 99 % sensitivity and specificity, positive and negative predictive values and likelihood ratios, cohort-related diagnostic failure and efficacy rates and the diagnostic yield of each TG parameter for different thrombophilias. RESULTS: A total of 467 patients were enrolled in the study, mostly investigated because of previous VTE (n = 283, 61 %). Thrombophilia testing was positive in 161/467 (35 %) patients. Normalized endogenous thrombin potential (ETP) effectively discriminated for ATD (AUC =79 [95 %CI 72-87]) and PTM (AUC 86 [95 %CI 79-93]) and ETP inhibition with thrombomodulin for PCD/PSD (AUC 90 [95 %CI 85-95]). With the established best performing TG parameter cut-offs, PCD/PSD, PTM, ATD, and low-risk APS could be safely (<3 % failure rate) excluded in 62 %, 58 %, 27 %, and 29 % of cohort patients, respectively. CONCLUSIONS: TG assessment using ST-Genesia® system shows promise as a supportive screening tool in thrombophilia work-up and warrants further validation.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".