Ocular tropism of SARS‐CoV‐2 infection
Bibliographic record
Abstract
COVID-19, caused by SARS-CoV-2, is still ravaging and has brought unprecedented challenges globally, which can lead to respiratory tract infections, pulmonary inflammation, and multiorgan involvement. 1 More COVID-19 patients with conjunctivitis as the first or concomitant symptom have been observed since the optimization and adjustment of COVID-19 measures in China on December 7, 2022.The following are three typical cases: Case 1: An 8-year-old boy from Ningbo, Zhejiang, China.He was infected by SARS-CoV-2 and visited Ningbo Beilun Second People's Hospital on January 1, 2023, complaining of pain in both eyes, foreign body sensation, blurred vision, tearing, periocular erythema, and suspected drug allergy.Examination revealed no obvious signs of a food or drug allergy.Given the patient's clinical history and symptoms, an ophthalmology referral was required.Case 2: A 30-year-old male from Ningbo visited the above hospital the same day.The COVID-19 patient complained of swollen, sore eyes, increased tear production, and eye secretions the day after the high fever.Case 3: A 50-year-old male patient from Wuhan visited Tianyou Hospital on January 2, 2023, after a high fever accompanied by sore, congested eyes, blurred version and tearing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".