Dyschromatopsia and contrast sensitivity changes in COVID-19 patients
Bibliographic record
Abstract
PURPOSE: To evaluate the alterations in the psycho-physical tests, contrast sensitivity (CS) and hue discrimination (total estimation score, TES) in COVID-19 patients. METHODS: A prospective case-control study was undertaken in 2021-22 to look at CS and TES among COVID-19 patients and RT-PCR negative normal subjects by a mobile app "smart optometry" and X-Rite's free online version of Farnsworth-Munsell 100 hue test. Strict visual acuity (minimum of 6/9 equivalent on Snellen) and other clinical parameters were used as exclusion criteria to filter potentially confounding pre-receptoral co-morbid conditions. The effect of mismatch in recruiting age and sex matched controls during the pandemic were analyzed by multivariate linear regression. RESULT: One-way ANOVA ruled out any influence of gender on CS and TES; however, there was significant difference in the TES on Mann Whitney U test (TES- 2.95 +/- 3.8 for cases; 0.30 +/- 1.1 for controls; p <0.001) and it persisted after accounting for age. Six controls with breakthrough infections had a significant deterioration in TES (Mean scores -3.0; 95% CI = -5.89 to -0.11; p = 0.04) on paired t test. Qualitative analysis showed that S-cone mediated deficiencies outnumber those affecting M-L cone mediated deficiencies. Among 53 subjects exhibiting three sensory features, anosmia, loss of taste and dyschromatopsia, nine (16.98%) had all features concurrently. CONCLUSION: The study demonstrates that COVID-19 infection leads to altered TES representing dyschromatopsia -an ocular counterpart of anosmia, with little difference on CS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".