Eye See Eye Learn The Benefit of Comprehensive Eye Examinations for Preschoolers
Bibliographic record
Abstract
Objective: Undetected vision problems in children can lead to permanent vision loss, a condition known as amblyopia. Early detection and treatment of the causes of amblyopia may prevent this vision loss. The objective of this paper is to look for evidence that comprehensive eye examinations upon entry to junior Kindergarten are an effective way to identify and treat vision problems early. methods: Relevant peer-reviewed publications on amblyopia and the importance of comprehensive eye examinations were reviewed. Specific areas investigated include: the prevalence and causes of amblyopia; impact of vision problems on child development and education; impact of amblyopia and/or strabismus on quality of life; and the cost effectiveness of treating amblyopia. The validity of vision screening compared to a comprehensive eye examination was also reviewed. Synthesis:The review suggests that without a complete eye examination many eye or vision problems remain undetected at school entry. Left uncorrected these problems negatively impact child development, education and quality of life. Reduced vision due to amblyopia also restricts future employment opportunities and increases the risk of bilateral visual impairment in adulthood. Examination procedures with high sensitivity and specificity are required to accurately detect these problems. Studies show that amblyopia treatment initiated at an early age is one of the most cost-effective of all health interventions. Conclusion: There is good evidence in the literature that a full eye examination is critical to detect all cases of amblyopia. This and other visual problems can be detected and managed at an early age, which leads to better visual quality of life and economical outcomes. The Eye See Eye Learn program offers the “gold standard” of eye care.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 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".