A Systematic Review on the Association Between Tear Film Metrics and Higher Order Aberrations in Dry Eye Disease and Treatment
Bibliographic record
Abstract
We systematically reviewed published research on dry eye disease and its association with higher order aberrations (HOAs). The purpose of this review was to first determine if an association between tear film metrics and HOAs exists and second to determine if the treatment of dry eyes can improve tear film metrics and HOAs together. A search was conducted in Entrez PubMed on 25 April 2021 using the keywords "higher order aberrations" and "dry eye". The initial search yielded 61 articles. After publications were restricted to only original articles measuring HOA outcomes in patients with dry eye, the final yield was 27 relevant articles. Of these 27 papers, 12 directly looked at associations and correlations between dry eyes and HOA parameters. The remaining 15 studies looked at dry eye interventions and HOA outcomes and parameters. There is clear evidence demonstrating that dry eyes and HOAs have an association, and that the tear film is one of the most important factors in this relationship. There is also a direct correlation between tear film metrics and HOAs. Improvements in HOAs with dry eye interventions provide further evidence to support the intricate relationship between the two. Despite the clear association between HOAs and dry eye disease, further research is still required in the realm of clinical application as dry eye interventions vary depending on many factors, including patient severity and eye drop viscosity.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".