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The Worcester Venous Thromboembolism Study: A Population Based Perspective of Venous Thromboembolism Attack Rates.

2005· article· en· W2592554342 on OpenAlexaff
Frederick A. Spencer, Robert J. Goldberg, Cathy Emery, Darleen Lessard, Apar Bains, Richard C. Becker, Frederick A. Anderson

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsMedicinePulmonary embolismPopulationDeep veinVenous thromboembolismVenous thrombosisCensusThrombosisMedical recordDemographyEmergency medicineSurgeryEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background: Although increasingly recognized as a major clinical problem, most reported estimates of the attack rates of venous thromboembolism (VTE) are based on studies enrolling patients more than a decade ago. Given changes in patient characteristics, risk factor profiles, and prophylaxis strategies over time, more current estimates are needed if we are to better target high-risk patients and allocate limited health care resources. The purpose of this study was to describe crude, as well as age and gender adjusted, attack rates of deep vein thrombosis (DVT) and pulmonary embolism (PE) in residents of the Worcester Statistical Metropolitan Area (SMSA) during the year 1999. Methods: The medical records of all male and female residents from the Worcester SMSA (2000 census = 478,000) diagnosed with ICD-9 codes consistent with possible DVT and/or PE at all 11 greater Worcester hospital during 1999 were reviewed by trained data abstractors. Characterization of each case of VTE was classified as definite, probable, or possible using prespecified criteria. For purposes of this analysis we approximated attack rates for the total Worcester SMSA population. However, for several specific analyses we have excluded 15 cases of validated VTE occurring in patients < 25 years of age. Age and sex-specific attack rates were calculated in a standard manner. Attack estimates were based on 2000 Massachusetts Census data for the Worcester SMSA which reported 287,631 residents 25 years of age or older. Results: There were a total of 590 recognized episodes of VTE in residents of the Worcester SMSA yielding an approximate attack rate of 123/100,000 population. Approximately one quarter of patients developed VTE during hospitalization for another indication while the remaining three quarters presented to the hospital with VTE. Excluding 15 cases of VTE occurring in patients < 25 years of age yields an attack rate of 200 per 100,000 population (95% C.I. 184, 216). Our study sample included 420 cases of isolated DVT (146/100,000 population), 140 cases of PE with or without DVT (49/100,000 population), and 74 cases of recurrent DVT (26/100,000 population). Overall, attack rates of DVT and PE for females were similar to those of men (DVT 152/100,000 vs 139/100,000; PE 51/100,000 vs 45/100,000). However attack rates in females age 75 years and older were significantly greater than those in men of the same age. The age and specific attack rates of clinically recognized VTE are shown in Figure 1. Conclusions: The annual overall attack rate of VTE in this community based study was slightly higher than that reported in the initial Worcester DVT study of 1985/1986 (107/100,000). In addition, if one excludes the small number of cases of VTE occurring in the young, attack rates/100,000 are almost doubled and increase rapidly with age particularly in women. These data have important implications for targeting of VTE prophylaxis and utilization of health care resources. Attack rate of clinical recognized VTE per 100,000 population: The Worcester Venous Thromboembolism Study 1999 Attack rate of clinical recognized VTE per 100,000 population: The Worcester Venous Thromboembolism Study 1999

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)0.004
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.312
Teacher spread0.291 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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