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Record W2791099148 · doi:10.1503/cmaj.170994

Risk of stroke in patients with dengue fever: a population-based cohort study

2018· article· en· W2791099148 on OpenAlexvenueno aff
Hao-Ming Li, Ying-Kai Huang, Yuan‐Chih Su, Chia‐Hung Kao

Bibliographic record

VenueCanadian Medical Association Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsnot available
Fundersnot available
KeywordsDengue feverStroke (engine)CohortPopulationMedicineDengue hemorrhagic feverCohort studyStroke riskMedical emergencyDengue virusVirologyEnvironmental healthInternal medicineIschemic stroke

Abstract

fetched live from OpenAlex

BACKGROUND: Stroke is a severe neurologic complication of dengue fever, described in only a few case reports. The incidence and risk factors for stroke in patients with dengue remain unclear. We conducted a population-based retrospective cohort study to investigate the risk of stroke in patients with dengue. METHODS: Using data from the Taiwan National Health Insurance Research Database, we included a total of 13 787 patients with dengue newly diagnosed between 2000 and 2012. The control cohort consisted of patients who did not have dengue, matched 1:1 by demographic characteristics and stroke-related comorbidities. We calculated the cumulative incidences and hazard ratios (HRs) of stroke in both cohorts using Kaplan-Meier curves and Cox proportional hazards regression. RESULTS: The overall incidence rate of stroke was 5.33 per 1000 person-years in the dengue cohort and 3.72 per 1000 person-years in the control cohort, with an adjusted HR of 1.16 (95% confidence interval [CI] 1.01-1.32). The risk of stroke among patients with dengue was highest in the first 2 months after diagnosis (25.53 per 1000 person-years, adjusted HR 2.49, 95% CI 1.48-4.18). INTERPRETATION: Dengue fever was associated with an increased risk of stroke in the first few months after diagnosis. The effect of dengue on stroke may be acute rather than chronic.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.002
GPT teacher head0.217
Teacher spread0.214 · 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 teacher head, not a consensus.

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

Citations36
Published2018
Admission routes1
Has abstractyes

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