Functional consequences of amblyopia and its impact on health-related quality of life
Bibliographic record
Abstract
• Amblyopia has lasting functional consequences beyond impaired visual acuity. • Amblyopia can create burden on patients, families, and healthcare systems. • Recognizing amblyopia’s impact will improve diagnoses and targeted intervention. Amblyopia (lazy eye) is the most common cause of monocular vision loss, affecting up to 4% of children and often persisting into adulthood. While treating the visual acuity deficit is often the focus of treatment, there is a pressing need for researchers, educators, and clinicians to understand the effects of amblyopia that extend beyond visual acuity. This review article highlights recent advances in understanding the impact of amblyopia on everyday life functioning. Amblyopia can significantly interfere with contrast sensitivity, attention, reading, eye-hand coordination, body composition, physical activity, and health-related quality of life. A deeper understanding of the functional consequences of amblyopia can be applied to patient management and inform amblyopia treatment, as well as support research into more effective interventions to prevent or rehabilitate deficits that can hinder children’s physical, social, and academic success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".