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Record W4225014132 · doi:10.5539/gjhs.v14n5p71

Adherence to Anticoagulation Ambulatory during the Beginning of Coronavirus Pandemic

2022· article· en· W4225014132 on OpenAlexvenueno aff
Suélen Feijó Hillesheim, Luiz Carlos Carneiro Pereira, Lorenzo Link Saldanha, Gabriela Vaz Pereira, Marco Aurélio Lumertz Saffi, Diego Chemello

Bibliographic record

VenueGlobal Journal of Health Science · 2022
Typearticle
Languageen
FieldComputer Science
TopicHealthcare during COVID-19 Pandemic
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAtrial fibrillationContext (archaeology)AmbulatoryPopulationPandemicEmergency medicineIntensive care medicinePhysical therapyInternal medicineCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

INTRODUCTION: Treatment with Vitamin K Antagonists is already proven to be significant in reducing thromboembolic events in patients with indications for systemic anticoagulation. In the context of ambulatory adherence, the SARS-CoV2 coronavirus pandemic emerged as a challenge for managing patients undergoing anticoagulation. METHODS: To avoid crowding and follow the recommended measures of social distancing, the Anticoagulation Ambulatory of the Hospital Universitário de Santa Maria started to adopt a differentiated model of care, with a collection of hospital prothrombin time and immediate home return, followed by teleconsultation, with medication adjustment, as necessary. The present study aimed to assess adherence to the new care model, as well as the profile of patients seen. A retrospective cohort study was conducted, analyzing consultations between March and May 2020. RESULTS: The results demonstrate a low-educated population (76% had completed elementary school at most). The most common indications for the use of oral anticoagulation were atrial fibrillation (33.6%), followed by mechanical aortic valve prosthesis (31.3%) and mechanical mitral valve prosthesis (17.2%). Regarding adherence, especially to blood collection in the scheduled period, there was low adherence in the initial weeks of the pandemic declaration, with substantial improvement in adherence in the last two weeks of analysis. CONCLUSION: Determining the clinical characteristics and profile of patients helps to determine the local needs of this population and implement active search strategies to improve adherence rates, which may reduce unfavorable outcomes.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.137
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.071
GPT teacher head0.390
Teacher spread0.318 · 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.

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
Published2022
Admission routes1
Has abstractyes

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