Exploring the overlooked risk: Ocular health and alterations in women with polycystic ovary syndrome
Bibliographic record
Abstract
BackgroundPolycystic Ovary Syndrome is well known to cause various metabolic changes in the body; however, changes in the ocular surface are not fully understood or well-described in the existing literature. Hormonal disturbances resulting from PCOS may affect multiple ocular tissues, including the posterior segment, lacrimal and meibomian glands, cornea, and conjunctiva.ObjectiveThis paper aims to summarize the current knowledge and research regarding ocular alterations related to PCOS.MethodA comprehensive review of the existing literature was conducted by searching multiple databases, including Scopus, PubMed, and Google Scholar. Keywords such as "Polycystic Ovary Syndrome," "PCOS," "ocular surface," "dry eye," "meibomian gland dysfunction," and "ocular changes" were used. Relevant case reports and clinical studies were included to provide a comprehensive understanding of the ocular implications of PCOS.ResultsAmong the ocular changes associated with PCOS, dry eyes are the most common source of irritation and discomfort in affected individuals. Recognizing this association is crucial for eye care practitioners.ConclusionIdentifying the link between PCOS and dry eyes enables practitioners to develop personalized management plans for individuals with PCOS, potentially improving their eye health and comfort in longer run. When necessary, further evaluation or referral may be required for patients with PCOS-related ocular symptoms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".