Bibliographic record
Abstract
BACKGROUND: Dry eye conditions are now recognized as having multiple causes. A subtype-based dry eye diagnostic protocol was developed to determine the prevalence of dry eye and dry eye subtypes, and the effects of age and gender, in subjects presenting to clinical optometry practice. METHODS: Dry eye diagnostic criteria were: presence of one or more McMonnies dry eye survey primary symptoms, fluorescein tear break time < 10 s and rose bengal ocular surface staining. Dry eye subtype differential diagnosis was made predominantly on the basis of biomicroscopic signs. Subtype categories were: lipid anomaly dry eye (LADE), aqueous tear deficiency (ATD), primary mucin anomalies, allergic/toxic dry eye (ADE), primary epitheliopathies and lid surfacing/blinking anomalies (LSADE). RESULTS: Dry eye prevalence was 10.8% for n = 1584 subjects. Dry eye was significantly more prevalent in subjects 40 years or older (18.1%) compared with those < 40 years (7.3%) (p = 0.001). LADE was the most prevalent subtype (4.0%), followed by ADE at 3.1%, LSADE at 1.8%, and ATD at 1.7%. ATD was the only subtype with a significant gender prevalence difference, being more prevalent in women (p = 0.0023). The prevalence of LADE and ATD were significantly greater in those 40 years or older (p = 0.001 and p = 0.0023 respectively). CONCLUSIONS: The results of this study support a subtype-based approach to dry eye diagnosis and management in clinical practice.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".