Non-invasive Intraocular pressure monitoring with contact lens
Bibliographic record
Abstract
BACKGROUND: Glaucoma is the second leading cause of blindness in the world and the first leading cause of irreversible vision loss. Currently, the primary methodology of testing for the intraocular pressure (IOP) is during clinical office hours, which only provide a limited amount of information on the trends and fluctuations of the IOP. Therefore, a continuous monitoring system is required to properly determine the peaks of pressure and to negate any false results obtained by sparse, clinic hour testing. The objective of this study is to determine the ability of a newly designed contact lens with an embedded microchannel, to accurately measure the fluctuations in the IOP. METHODS: Experimentation was completed on fresh enucleated porcine eyes. The contact lens was placed on the porcine eye and utilising a camera the fluid movement, within the microchannel in the contact lens, was recorded. A micro-pressure catheter, threaded into the centre of the vitreous chamber, recorded the true IOP and was compared with the displacement of the indicator fluid within the microchannel. RESULTS: The contact lenses showed a consistent linear responsiveness to changes in IOP and robust to the effects of anatomical differences among eyes. The indicator fluid had an average fluid movement of 28 um/mm Hg between all the trials. Additionally, the devices showed the ability to measure both increases and decreases in IOP during cyclical fluctuations. CONCLUSION: The described inexpensive and non-invasive sensor is able to reliably monitor the IOP changes based on porcine eye model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".