JAK2V617F mutation for the early diagnosis of Ph− myeloproliferative neoplasms in patients with venous thromboembolism: a meta-analysis
Bibliographic record
Abstract
Recent studies suggested that JAK2V617F mutation is frequent in patients with splanchnic vein thrombosis (SVT) but not in patients with other venous thromboembolic events (VTE). However, whether screening for the JAK2V617F mutation in VTE patients is justified remains unclear. Therefore, we performed a systematic review to assess the frequency of JAK2 mutation in VTE patients and the role of JAK2V617F mutation in the diagnosis of myeloproliferative neoplasms. MEDLINE and EMBASE databases were searched. Two reviewers independently performed study selection and extracted study characteristics. Pooled odds ratios of case-control studies and weighted mean proportion of the prevalence of JAK2V617F mutation of uncontrolled series were calculated. Twenty-four studies involving 3123 patients were included. Mean prevalence of JAK2 mutation was 32.7% (95% confidence interval, 25.5%-35.9%) in SVT patients. JAK2 mutation was associated with increased risk of SVT (odds ratio, 53.98; 95% confidence interval, 13.10-222.45). Mean prevalence of JAK2 mutation in other VTE patients was low (range, 0.88%-2.57%). Presence of JAK2V617F mutation in SVT patients was associated with a subsequent diagnosis of myeloproliferative neoplasm in many patients. JAK2 mutation is strongly associated with SVT, and routine screening of JAK2 mutation appears to be indicated in these patients.
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.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.046 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".