Evaluation of Tearing in Oculoplastics Assisted by Tear Osmolarity Measurement
Bibliographic record
Abstract
PURPOSE: To determine the tear osmolarity in patients with tearing secondary to dry eye and other pathologies, and to determine the prevalence of dry eye disease among patients with tearing in an oculoplastics setting. METHODS: 108 eyes of 54 patients with a chief complaint of tearing were prospectively recruited. Subjects were excluded if they used eye drops or contact lenses within 2 hours of assessment, had a history of refractive surgery, an active ocular allergy, or evidence of a systemic disease which affects tear production. A full medical and ocular history was taken with a complete eye exam pertinent to dry eye. Tear osmolarity was measured using the TearLab device. A clinical diagnosis of dry eye was made based on findings, without reference to tear osmolarity. RESULTS: Among 86 eyes symptomatic for tearing, 32 eyes had dry eye disease (37%). Patients with dry eye had a significantly higher median tear osmolarity compared to that in patients with other diagnoses (308 mOsm/L vs. 294 mOsm/L, p < 0.0001). At a cut-off of 308 mOsm/L, tear osmolarity resulted in a sensitivity of 50% and a specificity of 88% for the diagnosis of dry eye. CONCLUSIONS: A significant proportion of patients with tearing in an oculoplastics practice had dry eye disease. The high specificity of tear osmolarity may render it a useful tool to rule in dry eye disease and may assist the oculoplastic surgeon in more accurately determining the cause of tearing.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".