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Non-invasive Intraocular pressure monitoring with contact lens

2019· article· en· W2955320613 on OpenAlexafffund
Angelica Campigotto, Stephane Leahy, Guowei Zhao, Yongjun Lai

Bibliographic record

VenueBritish Journal of Ophthalmology · 2019
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsKingston Health Sciences CentreQueen's University
FundersQueen's University
KeywordsMedicineIntraocular pressureContact lensGlaucomaOphthalmologyMicrochannelLens (geology)OptometryBiomedical engineeringOpticsMaterials science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.250
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations38
Published2019
Admission routes2
Has abstractyes

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