The Agreement Between Self-Assessment and Clinician Assessment of Dry Eye Severity
Bibliographic record
Abstract
PURPOSE: The purpose of this analysis was to measure the degree of agreement between clinicians' assessment and subjects' self-assessment of dry eye severity in a cross-sectional, observational dry eye study. A secondary purpose was to identify the role of gender and age in that concordance. METHODS: In a cross-sectional observational study, 162 dry eye subjects and 48 controls were recruited from clinical databases of ICD-9 codes in 6 clinical sites. Before examination, subjects gave a global self-assessment of the severity of their dry eye from "none" to "extremely severe." After a clinical examination that included dry eye tests, the clinician discussed the subjects' symptoms and then gave global clinician assessment of dry eye from "none" to "severe." We measured the degree of agreement in these global measures. RESULTS: Although the correlation and agreement between clinician and self-assessment was significant (r = 0.720, P = 0.000; weighted K = 0.471; 95% CI = 0.395, 0.548; P = 0.000), the clinician assessment underestimated the severity in 40.9% of the subjects by at least 1 grade compared with the subjects' self-assessment. Over 54% of subjects over age 65 and 43% of the female subjects had their condition underestimated by the clinician (P < 0.05). CONCLUSIONS: Clinicians often relatively underestimated the severity of the subjects' self-assessment of dry eye in this clinical study, especially among the elderly and women. Eye care practitioners need better, more quantitative tools for the assessment of ocular surface symptoms to improve the concordance in severity assessment and to meet the needs of this symptomatic patient population by offering them appropriate treatments.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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, 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".