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Record W2620861527 · doi:10.1017/cjn.2017.147

P.063 The evolving epidemiology of infective endocarditis at St. Paul’s Hospital and Vancouver General Hospital

2017· article· en· W2620861527 on OpenAlexaffvenueabout
D Li, Graham Walker, Guoxing Xu, Douglas H. Johnston

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2017
Typearticle
Languageen
FieldMedicine
TopicInfective Endocarditis Diagnosis and Management
Canadian institutionsVancouver Biotech (Canada)
Fundersnot available
KeywordsMedicineEpidemiologyInfective endocarditisPopulationEndocarditisPediatricsCohortRetrospective cohort studyDemographyRochester Epidemiology ProjectInternal medicineSurgeryPopulation based study

Abstract

fetched live from OpenAlex

Background: SPH and VGH are the two largest tertiary care centers in BC’s Lower Mainland. Among those served are the low-SES, high-risk population of Vancouver’s Downtown East Side (DTES). We aim to characterize the changing epidemiology of infective endocarditis (IE) in this population from 1995 and 2014. To date, our database is among the world’s largest. Methods: 1337 cases were identified using ICD9/10 codes. A retrospective chart review was conducted to collect demographic data including HIV status, IVDU, neurologic complications and mortality. The cohort was dichotomized into IVDU and non-IVDU, and first (1995-2005) and second (2006-2014) decades. Data analysis was performed using univariate chi-square and t-tests. Results: Age at presentation has increased in the past decade (45 vs 55,p<0.001). Rates of IVDU and HIV have decreased significantly (50.5% vs 44.3%,p<0.001; 21.8% vs 7.9%,p<0.001, respectively). Neurologic complications were less frequent in non-IVDUs (16.5% vs 28.9%,p<0.01). Mortality was greater in those with neurologic complications (RR=2.6 95%CI:2.1-3.3,p<0.001). Patients with neurologic complications were more likely to undergo cardiac surgery (RR=1.6 95%CI:1.3-2.0,p<0.001). Conclusions: Our findings highlight the changing epidemiology of IE. Some discrepancies between our data and the existing literature may be accounted for by Vancouver’s unique DTES population. Further work characterizing this is ongoing.

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 score0.589
Threshold uncertainty score0.818

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.028
GPT teacher head0.290
Teacher spread0.262 · 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
Published2017
Admission routes3
Has abstractyes

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