Ocular manifestations and SARS-CoV-2 detection in tears and conjunctival scrape from non-severe COVID-19 patients
Bibliographic record
Abstract
AIM: To explore the ocular features of corona virus disease (COVID)-19 and severe acute respiratory syndrome coronavirus (SARS-CoV)-2 detection in tears and conjunctival scrapes in non-severe COVID-19 patients. METHODS: , 2020. Clinical data and samples of tears and conjunctival scraping were collected in consecutive laboratory-confirmed, non-severe COVID-19 patients from three hospitals. COVID-19 virus was analyzed by real-time reverse transcriptase polymerase chain reaction (RT-PCR) kits. RESULTS: Totally 255 laboratory-confirmed, non-severe COVID-19 patients were recruited for ocular manifestation investigation. Of them, 54.9% were females, with a mean age of 49.4y. None of the patients has evidence of uveitis; 11 patients (4.3%) complained of mild asthenopia; 2 (0.8%) had mild conjunctival congestion and serous secretion. Twenty-five of them had performed tears and conjunctival scrape for COVID-19 virus detection, with 4 yield possible positive results in the nucleoprotein gene. One of them were asymptomatic with normal chest CT and positive pharyngeal swab result. CONCLUSION: Ocular manifestations are neither common nor specific in non-severe COVID-19 patients. Meanwhile, COVID-19 virus nucleotides can be detected in the tears and conjunctival scrape samples, warranting further research on the transmissibility by the ocular route.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".