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Record W3017691845 · doi:10.1002/mds3.10086

A novel non‐invasive wearable sensor for intraocular pressure measurement

2020· article· en· W3017691845 on OpenAlex
Angelica Campigotto, Yongjun Lai

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMedical Devices & Sensors · 2020
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsQueen's University
FundersQueen's University
KeywordsIntraocular pressureGlaucomaMedicinePressure sensorOphthalmologyOptic nerveWearable computerComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract Glaucoma is a chronic eye disease where an increase in intraocular pressure (IOP) permanently damaging the optic nerve leading to irreversible vision loss. Intraocular pressure is the main factor for monitoring the progression of glaucoma and has been found to fluctuate throughout the day. A continuous monitoring system can track the fluctuations in the intraocular pressure throughout the day, improving the management of the disease. A novel non‐invasive wearable sensor was created to monitor the fluctuating corneal curvature of the eye and directly relate the deformation to the intraocular pressure. The wearable sensor was able to capture on average 40.8 µm/mmHg with a standard deviation of 29.4 in fluid location per increase in intraocular pressure with an ability to return over 80% back to its original position indicating a good ability to accurately track the fluctuations in the IOP.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.027
GPT teacher head0.259
Teacher spread0.233 · 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