Impact of COVID-19 pandemic control measures on amblyopia treatment: a retrospective study of records from a tertiary eye hospital in China
Bibliographic record
Abstract
OBJECTIVES: Amblyopia is the most common cause of unilateral visual impairment in children and requires long-term treatment. This study aimed to quantify the impact of pandemic control measures on amblyopia management. DESIGN AND SETTING: This was a retrospective cohort study of data from a large amblyopia management database at a major tertiary eye care centre in China. PARTICIPANTS: Outpatients with amblyopia who visited the hospital from 1 June 2019, through 28 February 2022. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the number of first and follow-up in-person visits to the hospital for amblyopia treatment. Secondary outcomes included the time interval between consecutive visits and improvement of vision (visual acuity, contrast sensitivity and stereopsis). Patient records were grouped into prepandemic and during pandemic periods. RESULTS: A total of 10 060 face-to-face visits for 5361 patients (median age 6.7 years, IQR 5.4, 8.9) that spanned two lockdown periods were included in the analysis, of which 28% were follow-up visits. Pandemic control measures caused a sharp decline in the number of outpatient visits (3% and 30% of prepandemic levels in the months directly after the start of the first (2020) and second (2021) periods of pandemic control measures, respectively). However, these drops were followed by pronounced rebounds in visits that exceeded prepandemic levels by 51.1% and 108.5%, respectively. The interval between consecutive visits increased significantly during the pandemic from a median (IQR) of 120 (112, 127) days in 2019 to 197 (179, 224) in 2020 (p<0.001) and 189 (182, 221) in 2021 (p<0.001). There were no significant differences in the improvement of visual function or treatment compliance between the prepandemic and postpandemic groups. CONCLUSIONS: The number of amblyopia patient hospital visits spiked well above prepandemic levels following lockdown periods. This pattern of patient behaviour can inform planning for amblyopia treatment services during and after public health-related disruptions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".