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Record W1726552965 · doi:10.1182/blood.v106.11.585.585

Determinants of Health-Related Quality of Life after Deep Venous Thrombosis: Two-Year Results from a Canadian Multicenter Prospective Cohort Study (The VETO Study).

2005· article· en· W1726552965 on OpenAlexaffabout
Susan R. Kahn, Tatiana N. Vydykhan, Donna L. Lamping, Thiérry Ducruet, Louise Arsenault, Marie José Miron, André Roussin, Sylvie Desmarais, Francine Joyal, Jeannine Kassis, Susan Solymoss, Jeffrey S. Ginsberg, Louis Desjardins, Mira Johri, Ian Shrier

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversité LavalMcGill UniversityUniversité de MontréalMcMaster UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineQuality of life (healthcare)CohortProspective cohort studyVenous thrombosisDeep veinPost-thrombotic syndromePhysical therapyThrombosisCohort studyInternal medicineIncidence (geometry)

Abstract

fetched live from OpenAlex

Abstract Background and Objectives: The long term effects of deep venous thrombosis (DVT) on health-related quality of life (QOL) have not been prospectively evaluated. During a Canadian multicentre cohort study of health and economic outcomes after DVT (the Venous Thrombosis Outcomes [VETO] Study), we measured QOL during the 2 years after DVT and evaluated the influence of development of post-thrombotic syndrome (PTS) and other determinants on QOL. Methods: Consecutive patients diagnosed with acute DVT at seven participating hospital centres were recruited from April 2001-July 2002. During study visits at Baseline, 1, 4, 8, 12 and 24 months, clinical data were collected, a standardized assessment for PTS was performed and QOL questionnaires were self-completed. Generic QOL was measured using the SF-36 Health Survey questionnaire. Venous disease-specific QOL was measured using the VEINES-QOL questionnaire. For both questionnaires, lower scores indicate poorer QOL. Mean QOL at each time point and change in QOL scores over 24 months follow-up were quantified. The influence of PTS and other clinical or demographic characteristics on QOL was evaluated using multivariate regression analyses. Results: Of the 359 patients recruited, 49% were male, average age was 56 years, 2/3 were outpatients and 55% had proximal DVT. The cumulative incidence of PTS during follow-up was 37%. On average, QOL scores in the cohort improved during follow-up (mean improvement from Baseline to 24 months: 7.2 points for SF-36 mental component summary (MCS), 7.5 points for SF-36 physical component summary (PCS) and 4.7 points for VEINES-QOL). Patients who developed PTS, compared with those who did not, had significantly lower PCS scores and VEINES-QOL scores at all 5 follow-up visits (differences for PTS vs. no PTS ranged from 2.4-9.0 points for PCS and from 4.5-6.0 points for VEINES-QOL), and significantly lower MCS scores at the 1, 4, 8 and 12 month visits (differences ranged from 3.7–4.7 points). Multivariate regression analyses adjusted for age, sex and comorbidity showed that PTS and proximal (vs. distal) location of initial DVT were strong independent predictors of lack of improvement of PCS scores (p=.003 and .02, respectively) and VEINES-QOL scores (p=.006 and .009, respectively) during follow-up, but not of MCS scores (p=0.054 and 0.86, respectively). Variables such as body mass index, concurrent pulmonary embolism at time of DVT diagnosis and previous history of DVT did not influence change in scores for any QOL measure. Conclusions: The principal determinants of quality of life after DVT are development of PTS and proximal (vs. distal) location of initial DVT. Our study provides valuable prognostic information on health outcomes after DVT.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.163
Threshold uncertainty score0.329

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.312
Teacher spread0.287 · 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
Published2005
Admission routes2
Has abstractyes

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