Cutaneous manifestations in elderly patients with confirmed coronavirus disease 2019 and the disease outcomes: A systematic review
Bibliographic record
Abstract
Cutaneous manifestations in coronavirus disease 2019 (COVID-19) patients may possess prognostic value for identifying potentially severe cases. This systematic review investigated whether dermatological features are associated with COVID-19 outcomes in elderly patients. Literature retrieval was conducted on May 11, 2022, from databases, hand-searching, and tracing citations. Following selection against eligibility criteria, the remaining records were evaluated utilizing the National Heart, Lung, and Blood Institute Study Quality Assessment Tool or Newcastle-Ottawa Scale. The pooled individual data were subsequently analyzed using the Cochran-Mantel-Haenszel test to calculate the odds ratio (ORs) and confidence intervals (CIs) for the severity and mortality of each skin lesion type. We incorporated 70 articles, including 180 and 117 entries, with information regarding disease severity and mortality. Further analysis revealed that vascular type was the skin lesion most frequently noticed in confirmed COVID-19 elderly patients (46.2%) and was associated with an increased risk of developing advanced disease (OR 7.32, 95% CI 3.39-15.81) and the ensuing termination (OR 5.73, 95% CI 2.46-13.36). The converse phenomenon was observed in maculopapular type (severity OR 0.27, 95% CI 0.14-0.52; mortality OR 0.10, 95% CI 0.03-0.36). In conclusion, skin manifestations may predict COVID-19 severity and mortality in the senior group.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".