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Record W4200063345 · doi:10.33069/cim.2021.0024

A Study on Chronofatality Trends of COVID-19 Deaths at a Tertiary Care Hospital

2021· article· en· W4200063345 on OpenAlexaff
Kattamreddy Ananth Rupesh, Mopuri Venkateswarlu, Suresh Pamujula, Pravin Panditrao Kalyankar, Nitesh Motiram Salvi, Shayali Vora

Bibliographic record

VenueChronobiology in Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsCanadore College
Fundersnot available
KeywordsMedicineDiabetes mellitusComorbidityCircadian rhythmDiseaseCase fatality rateCoronavirus disease 2019 (COVID-19)Cause of deathCoronary artery diseaseInternal medicineEmergency medicineEpidemiologyInfectious disease (medical specialty)Endocrinology

Abstract

fetched live from OpenAlex

The aim of the study is to find out any association between time of death in coronavirus disease (COVID-19) patients and variables like age, sex, and existence of comorbidities including type II diabetes mellitus, hypertension, coronary artery disease, chronic kidney disease, etc. An attempt was also made to elucidate the reasons for relationship between time of death and other aforementioned variables. Mortality data of 1,553 COVID-19 cases from a tertiary care hospital between March 2020 to September 2021 were analyzed. Maximum deaths occurred between 18:01 hours to 06:00 hours of the 24-hour cycle. There is a significant statistical association between time of death and age, time of death and sex, time of death and having a comorbidity of diabetes mellitus in the study sample. The study confirms that the chronofatality of COVID-19 deaths has a nocturnal predilection. The circadian rhythms of glucocorticoids, respiratory physiology of sleep, and circadian hemodynamic variations may have a role in prognosis and fatality of COVID-19.

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)0.001
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 score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.385
Teacher spread0.352 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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