Diagnostic accuracy of sinus tachycardia as an independent clinical indicator among different COVID-19 variants
Bibliographic record
Abstract
Background.The most common arrhythmia which have been reported frequently in COVID-19 patients is sinus tachycardia.As COVID-19 is usually misdiagnosed with other respiratory tract diseases, introduction of a rapid clinical indicator for out of proportional sinus tachycardia in the diagnosis of COVID-19 during the early viral replication stage is essential for better cost-effective use of resources.Objectives.This study was conducted to determine the diagnostic accuracy of sinus tachycardia as an independent indicator of COVID-19.Material and methods.This is a cross-sectional analytical study.It included 152 healthcare workers who fulfilled the inclusion criteria.Multiple logistic regression analysis was conducted to investigate the factors associated with COVID-19 among the entire study sample and among each group.Results.Among our participants, 32.9% were male, while 67.1% were female, with a mean age of 35.47 ± 7.09 years.It was found that 51.3% of our sample were COVID-19 PCR positive, and the mean number of days of symptoms at presentation was 2.01 ± 1.29.It was found that the prevalence of out of proportional sinus tachycardia among the participants diagnosed with COVID-19 in 2021 was triple that of the participants who were recruited in 2020 (61%, 26%, respectively).It was found that there was significant association between pulse rate and COVID-19, with gender, age, temperature or days of symptoms having no effect. Conclusions.The study highlights the diagnostic accuracy of sinus tachycardia as an independent indicator of COVID-19, especially the Omicron variant, as a higher pulse rate is associated with higher odds of having COVID-19 Key words: sinus tachycardia, COVID-19, cardiac arrhythmias.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".