Upcoming methods and specifications of continuous intraocular pressure monitoring systems for glaucoma
Bibliographic record
Abstract
Glaucoma is the leading cause of irreversible blindness and vision loss in the world. Although intraocular pressure (IOP) is no longer considered the only risk factor for glaucoma, it is still the most important one. In most cases, high IOP is secondary to trabecular meshwork dysfunction. High IOP leads to compaction of the lamina cribrosa and subsequent damage to retinal ganglion cell axons. Damage to the optic nerve head is evident on funduscopy as posterior bowing of the lamina cribrosa and increased cupping. Currently, the only documented method to slow or halt the progression of this disease is to decrease the IOP; hence, accurate IOP measurement is crucial not only for diagnosis, but also for the management. Due to the dynamic nature and fluctuation of the IOP, a single clinical measurement is not a reliable indicator of diurnal IOP; it requires 24-hour monitoring methods. Technological advances in microelectromechanical systems and microfluidics provide a promising solution for the effective measurement of IOP. This paper provides a broad overview of the upcoming technologies to be used for continuous IOP monitoring.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".