Corneal Nerve Assessment by Aesthesiometry: History, Advancements, and Future Directions
Bibliographic record
Abstract
The measurement of corneal sensation allows clinicians to assess the status of corneal innervation and serves as a crucial indicator of corneal disease and eye health. Many devices are available to assess corneal sensation, including the Cochet-Bonnet aesthesiometer, the Belmonte Aesthesiometer, the Swiss Liquid Jet Aesthesiometer, and the newly introduced Corneal Esthesiometer Brill. Increasing the clinical use of in vivo confocal microscopy and optical coherence tomography will allow for greater insight into the diagnosis, classification, and monitoring of ocular surface diseases such as neurotrophic keratopathy; however, formal esthesiometric measurement remains necessary to assess the functional status of corneal nerves. These aesthesiometers vary widely in their mode of corneal stimulus generation and their relative accessibility, precision, and ease of clinical use. The development of future devices to optimize these characteristics, as well as further comparative studies between device types should enable more accurate and precise diagnosis and treatment of corneal innervation deficits. The purpose of this narrative review is to describe the advancements in the use of aesthesiometers since their introduction to clinical practice, compare currently available devices for assessing corneal innervation and their relative limitations, and discuss how the assessment of corneal innervation is crucial to understanding and treating pathologies of the ocular surface.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".