Provocation of Dry Eye Disease during COVID-19 Lockdown
Bibliographic record
Abstract
Abstract To assess the impact of COVID-19 lockdown measures on dry-eye symptoms in a community-based population, a cross-sectional study was conducted during the first wave of the COVID-19 outbreak in Thailand. An online survey was distributed via social media between June and July 2020. The questionnaire elicited information on demographics, dry-eye symptoms, viewing habits, and mental health status. There were 535 respondents. Thirty-seven percent reported having been diagnosed with dry-eye disease (DED). During the lockdown, the mean dry-eye symptom score (DESS) of overall participants dropped significantly from 81.6±15.9 to 79.8±17.4 (P<0.001). The mean, daily, visual display terminal (VDT) usage increased from 10.55±5.16 hours to 13.08±5.65 hours (P<0.001). A negative correlation between age and VDT usage was observed in both the normal and lockdown situations. One-quarter of all participants had an abnormal mental health status. The female gender (OR 1.86; 95% CI 1.14–3.04) and increased VDT usage during the lockdown (OR 5.68; 95% CI 3.49–9.23) were independently associated with worsening dry-eye symptoms. The lockdown measures abruptly altered the behaviors and lifestyles of the overall population. Excessive exposure to VDTs were associated with deteriorated dry-eye symptoms, and it possibly contributed to the increased DED incidence in the general population.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".