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Record W4210583135 · doi:10.5539/gjhs.v14n3p12

COVID-19 in Socially Vulnerable Patients with Tuberculosis in Brazil

2022· article· en· W4210583135 on OpenAlexvenueno aff
Eliza Miranda Ramos, Emerson Luiz Lima Araújo, James Venturini, Gilberto Gonçalves Facco, Cinthya Cristina de Oliveira Canuto dos Reis, Grazielle Franco Ferro da Costa Rodrigues, Antônio Carlos de Abreu, Francisco José Mendes dos Reis, Pamela Aline Miranda Teodoro, Ernani Mendes da Fonseca, Rubens César Ferreira Pereira, Valter Aragão do Nascimento

Bibliographic record

VenueGlobal Journal of Health Science · 2022
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTuberculosisCoronavirus disease 2019 (COVID-19)Tuberculosis controlPulmonary tuberculosisPopulationCohortSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Internal medicineSurgeryPathologyEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION: COVID-19 is a global public health problem. The first case reported in the socially vulnerable population in the territory of Mato Grosso do State/Brazil occurred in August 2020. However, information about the co-infection of Tuberculosis (TB) and COVID-19 is rarely described in the world literature. Objective: to describe for the first time a group of vulnerable patients who died or not due to Tuberculosis and COVID-19, using a Cohort study of a control group and a treatment group. METHODS: Reporting 7 cases of TB associated with COVID-19 confirmed by respiratory RT-PCR, hospitalized in a hospital department of COVID-19 (CEAA: 42969320.0.0000.0021). RESULTS: The patients included in this case report were aged between 19 and 83 years, respectively, with a predominance of females, 4 patients were vaccinated with BCG. In addition, 3 patients died from COVID-19, and 2 patients were considered cured by COVID-19. The mean time to diagnosis between Tuberculosis and COVID-19 in the “control group” was 43 days and the mean time to diagnosis in the “treatment group” was 37 days. The average number of days of hospital stay in the “control group” was 50 days and in the “treatment group” it was 40 days. In the patients in the “control group”, 2 presented a unilateral pulmonary cavity lesion on the X-ray. In the “treatment group”, only 1 patient presented a unilateral pulmonary cavity lesion on the X-ray, and 2 presented a bilateral pulmonary cavity lesion, and only one developed bilateral non-cavitary lesion with infiltrates. Regarding drug resistance to the treatment of Tuberculosis, in the “treatment group” only 2 patients were sensitive and 3 were resistant. On the other hand, in the “control group”, 1 patient presented resistance and 1 sensitivity. It was found that 4 patients with drug resistance to Tuberculosis died (57%). Among the patients who died, it was observed that patients aged 83, 70 years and 66 years were not vaccinated with Bacillus Calmette-Guérin (BCG). It was found in this case report that patients with COVID-19 and Tuberculosis have 60% less chance of cure. Furthermore, patients cured with pulmonary sequelae of COVID-19 may be at greater risk of developing advances in the worsening of Tuberculosis in the future. However, according to the results found in this case report, in some socially vulnerable regions where the forms of advanced Tuberculosis occur there is the presence of drug-resistant Mycobacterium Tuberculosis strains. CONCLUSION: Our study showed that Mycobactrium tuberculosis (MTB) infection in the socially vulnerable population increased the susceptibility to SARS-CoV-2 and the severity of COVID-19 or vice-versa. Based on this case report, it is recommended that, in the socially vulnerable population, the status of MTB infection be verified in patients with suspected COVID-19 at hospital admission.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.030
GPT teacher head0.399
Teacher spread0.370 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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