Novel Ocular Thermography Metrics for Dry Eye Screening
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it