Novel Ocular Thermography Metrics for Dry Eye Screening
Bibliographic record
Abstract
Purpose: This study investigated the efficacy of automated ocular thermography metrics for the screening of dry eye disease (DED). Methods: This was a prospective study that enrolled 20 participants with DED, sex- and age-matched to 20 non-DED controls. Ocular Surface Disease Index (OSDI), Dry Eye Questionnaire-5 (DEQ5), noninvasive tear-break-up time (NITBUT), tear meniscus height (TMH), meibomian gland dysfunction (MGD) score, and corneal staining were measured in a screening visit. The DED group was defined as: OSDI score of ≥13 or DEQ-5 score of ≥6, and DED signs in at least one eye (corneal/conjunctival/lid margin staining, NITBUT <5 seconds, tear film osmolarity ≥308 miliosmoles [mOsm]/L). Thermography recording of the ocular surface (natural blinking over a period of 30 seconds) was conducted the next day, and the thermal cooling rate and thermal interblink interval (IBI) were derived. Results: Thermal IBI was significantly shorter in the DED group compared to the non-DED group (P = 0.034). The thermal cooling rate was significantly faster in the DED group (P = 0.047). Thermal IBI significantly correlated with DEQ5 (r = -0.37, P = 0.025) and OSDI (r = -0.37, P = 0.026). The thermal cooling rate significantly correlated with DEQ5 (r = -0.39, P = 0.022) and OSDI (r = -0.36, P = 0.036). The best discrimination was achieved by combining the thermal cooling rate and TMH, with an area under the curve (AUC) = 0.80 (sensitivity = 0.87 and specificity = 0.63). Conclusions: The thermal IBI and thermal cooling rate were significant predictors of DED, suggesting the utility of ocular thermography for DED screening. Translational Relevance: Automated ocular thermography may help to assess ocular dryness in a noninvasive, quantifiable, and real-time manner.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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, 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".