Potency of Anosmia and Ageusia as Covid-19 Prognostic Factors: A Systematic Review
Bibliographic record
Abstract
Introduction: The clinical signs of COVID-19 include ageusia and anosmia. Anosmia and ageusia haven’t been evaluated as prognostic factors in any prior studies, though. Therefore, the purpose of this review is to assess the effectiveness of ageusia and anosmia as prognostic indicators in COVID-19 patients. Methods: Literature was collected from various databases systematically using the PRISMA until May 25th,2022. The screening process was performed based on inclusion and exclusion criteria, before being analyzed qualitatively. The risk of bias was assessed using Newcastle-Ottawa Quality Assessment Scale converted by AHRQ. Results: Anosmia and ageusia could be used as the indicator for the good prognostic associated with lower mortality, milder trajectory rate, ICU, and hospital admission risk, and shorter length of stay. Anosmia and ageusia have shown high prevalence to predict a prognosis for the COVID-19 infection. Although COVID-19 prognosis also depends on the other lying conditions, patients with anosmia or ageusia had a lower mortality risk due to the lower body mechanism and cell inflammation mechanism toward the viral load that may not lead to the maladaptive cytokine release in response to infection generally called as a cytokine storm. Conclusion: In COVID-19 patients, anosmia and ageusia have been shown to be indicators of a favorable prognosis due to lower disease severity, mortality, risk of ICU and hospital admission, and shorter duration of stay. Therefore, in order to determine the prognosis, it is important to assess the clinical symptoms of the patients.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".