Optic nerve head alterations after COVID-19: an optical coherence tomography angiography-based longitudinal study
Bibliographic record
Abstract
OBJECTIVE: To evaluate longitudinal changes in peripapillary vessel density (VD) following coronavirus disease 2019 (COVID-19) using optical coherence tomography angiography. METHODS: As part of a prospective longitudinal observational study, we studied healthy individuals with a history of mild COVID-19 confirmed via real-time polymerase chain reaction. After recovery, we used the Optovue RTVue XR Avanti machine to perform optic nerve head (ONH) imaging. We also assessed the VD of all vessels and of small vessels in the disc and the radial peripapillary capillary (RPC) network at 1 and 3 months post-recovery. RESULTS: We included 17 patients (34 eyes; mean age: 36.9 ± 10.2 years, range: 24-62 years) who had recovered from COVID-19. No changes were observed in the ONH parameters. However, there was a noticeable trend of increased small vessel VD values in the RPC. These increases were significant for the peripapillary whole, superior hemifield, inferior-temporal, temporal-superior, and superior-temporal small vessels. Moreover, the evaluation of all vessel VD values in the RPC revealed a significant decrease in the inside disc and a significant increase in a grid-based inferior region. CONCLUSION: COVID-19 may affect VD of the RPC in the ONH, and should be considered in ONH evaluations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".