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Record W2556287997 · doi:10.1182/blood.v104.11.491.491

A Canadian Multicenter Case-Control Study of the Risk of Venous Thrombosis in Car and Airplane Travelers.

2004· article· en· W2556287997 on OpenAlexaffabout
Lucie Opatrny, Stanley H. Shapiro, Marie‐José Miron, Yury Monczak, Susan R. Kahn

Bibliographic record

VenueBlood · 2004
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsHôpital Notre-DameJewish General Hospital
Fundersnot available
KeywordsMedicineConfoundingOdds ratioBody mass indexRisk factorEpidemiologyVenous thrombosisLogistic regressionInternal medicineThrombosisSurgery

Abstract

fetched live from OpenAlex

Abstract Background: The potential association between venous thromboembolism (VTE) and travel, particularly air travel (“economy class syndrome”), has been the subject of extensive media coverage. While it is biologically plausible that prolonged travel is an independent risk factor for VTE, epidemiological data to date are conflicting, and confounders have rarely been accounted for. Aim: To determine whether there is a greater risk of exposure to travel in patients with confirmed DVT compared with patients in whom DVT is ruled out. To examine the influence of confounding variables on the relation between DVT and travel. Methods: This was a Canadian multi-center case control study. Consecutive patients presenting to the vascular laboratory with clinically suspected DVT were eligible to participate. Cases were patients with objectively confirmed DVT on venous ultrasound; controls were patients in whom DVT was ruled out. Detailed recent travel history, medications and clinical characteristics were obtained via standardized, interviewer-administered questionnaires. Genetic testing for Factor V Leiden and Prothrombin gene mutations was performed. Unconditional multivariate logistic regression analyses with adjustment for confounders and testing for interactions were performed to examine the relation between DVT and (1) any travel, and (2) duration of travel. Plane and car travel were also analyzed separately. Results: There were 359 cases and 359 controls. Mean age among cases was 56 years and 50% were male. Among controls, mean age was 64 years and 35% were male. Body mass index, smoking status and patient location (inpatient vs. out-patient) were comparable between the two groups. The crude and adjusted odds ratio (OR) for exposure to travel in cases was 1.15 (95% confidence interval (CI): 0.78, 1.69) and 1.44 (95%CI: 0.86, 2.40), respectively. Travel of ≥ 12 hours’ duration was associated with a higher OR (adjusted OR 2.92, 95%CI: 0.54, 15.73) than shorter travel durations (adjusted OR 1.29; 95%CI: 0.62, 2.66). Analyzing plane and car travel separately showed that the adjusted OR for plane travel was 2.28 (95%CI: 0.94, 5.50) but for car travel was 1.00 (95%CI: 0.54, 1.83). Increasing durations of plane travel, but not car travel, resulted in higher ORs. For plane travel ≥ 12 hours, the crude OR was 8.22 (95%CI: 1.02, 66.05) and the adjusted OR was 7.10 (95% CI: 0.70, 72.35). No statistical interactions were detected between travel and thrombophilia, hormonal therapy, or clinical VTE risk factors. Interpretation: This is the largest case control study to date of the relation between DVT and travel that takes into account concurrent DVT risk factors. Plane travel but not car travel appears to be a mild independent risk factor for DVT. However, flights of 12 hours or longer were associated with a 7-fold increased risk of DVT. Our findings may have future implications regarding the use of thromboprophylaxis during long-haul travel.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.009
GPT teacher head0.228
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2004
Admission routes2
Has abstractyes

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