Reply to Letter to Editor: Unilateral Acute Central Serous Chorioretinopathy with Inactivated Coronavirus Disease 2019 Vaccination – A Case Report and Review of Literature
Bibliographic record
Abstract
Dear Editor, We thank the authors for their comments regarding our article entitled “Unilateral acute central serous chorioretinopathy with inactivated coronavirus disease 2019 (COVID-19) vaccination: A case report and review of literature”.1 We presented a case report of a 39-year-old man who developed central serous chorioretinopathy (CSC) 2 days after receiving the Sinopharm vaccine, and we acknowledge that the relationship between COVID-19 vaccination and ocular side effects is worth studying. While we considered the probability of coincidence due to the patient’s age and gender, we agree that the influence of confounding variables and the pathopharmacological relationship between the vaccine and the clinical problem can be challenging to determine without sufficient clinical data on vaccine recipients’ physiological and immunological status before vaccination. However, we found no modifiable risk factors in the patient’s history for developing CSC. We believe that a longitudinal case–control study on a large number of participants is necessary to establish the true link between vaccination and these reported side effects. However, due to ethical concerns about vaccinating individuals against infectious diseases like COVID-19, which have proven benefits in decreasing the incidence and severity of infection,2 such studies may be challenging to conduct. Nevertheless, it is crucial to collect and report such cases to determine causality over coincidence. In addition, ophthalmologists must pay attention to relevant points in patient history when approaching similar cases. Clues that help to prove causality include time series, biological validity, dose-response relation, and findings concurrence. The autoimmune nature of all reported cases of presumed COVID-19 vaccine side effects is another factor that may help understand the causality. Furthermore, CSC has been reported after vaccination for influenza, yellow fever, anthrax, and smallpox; however, the causality has not been established in a proper manner.3–5 Thank you for your valuable comments on this important issue. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".