Ocular manifestations in SARS-CoV-2 positive patients: a systematic review
Bibliographic record
Abstract
Objective: Investigate the importance of optical symptoms within SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) patients.Design: Systematic reviewMethods: Databases searched included Medline, EMBASE, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Clinicaltrials.gov, ProQuest. As well, meeting abstracts from American Academy of Ophthalmology, Association for Research in Vision and Ophthalmology and Canadian Ophthalmological Society were also considered. Articles underwent two rounds of screening before risk of bias assessment and data extraction.Results: In total, 582 studies were identified. A total of 2064 unique SARS-CoV-2 positive patients are included in this review from 13 different studies that met the inclusion criteria. We observed that the most common ocular symptoms in patients with SARS-CoV-2 were dry eyes, chemosis, epiphora, and blurred vision. The least common symptoms included hyperemia, conjunctivitis and photophobia. Additionally, we observed a unique relationship between patients with ocular manifestations and the severity of the systemic symptoms.Conclusion: Ocular symptoms do not occur commonly among SARS-CoV-2 positive patients; however, this study displays that there is an occurrence of common ocular manifestations such as dry eyes, chemosis, epiphora, and blurred vision. Therefore, increased ocular examinations may aid in diagnosis of SARS-CoV-2 infections.ABBREVIATIONS: COVID-19 (coronavirus disease 2019), SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2)
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".