May‐Thurner syndrome prevalence as a risk factor for acute deep vein thrombosis is unknown
Bibliographic record
Abstract
An excellent review on May-Thurner syndrome prevalence has recently been published and described the challenge to establish a correlation between significant left common iliac vein compression and clinical outcomes.1 The authors mention the condition is responsible for 2% to 5% of all deep vein thromboses (DVTs).1 However, we want to point out that May-Thurner syndrome (iliac vein compression syndrome or iliocaval compression syndrome) prevalence as a responsible risk factor for all DVTs is most likely unknown. The references cited in the article refer to two unicentric cohort studies led by the same author in which 18/900 (2%) patients and 40/800 (5%) patients presenting with presumed lower extremities’ venous disorders had features compatible with iliocaval compression.2, 3 Not every patient was assessed by venography and detailed demographics and other risk factors for DVT were not provided. Moreover, diagnosis explaining patients’ leg symptoms were not fully described and presumed DVTs were only mentioned in the second article in which 6/40 patients would have had a phlebitis (without further descriptions). Hence, we cannot assume 2% to 5% of all DVTs are explained by May-Thurner syndrome while others important variables from such a population were not assessed. Another review on lower extremity venous thrombosis diagnosis has also fueled this prevalence information regarding the responsibility of iliac vein compression syndrome in DVTs.4 We should avoid using these unvalidated presumed prevalence numbers that give a false assumption that we understand the possible magnitude of its impact as a risk factor for acute DVTs. GR has received honoraria from BMS/Pfizer, Bayer, and Servier.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".