Polarimetric Interferometry for Assessment of Corneal Stromal Lamellae Orientation
Bibliographic record
Abstract
PURPOSE: To analyze corneal stromal lamellae orientation in normal subjects and patients with various corneal conditions using a polarimetric interferometer. METHODS: Thirty-two healthy control subjects (59 eyes) and 16 patients (22 eyes) with corneal conditions or postkeratoplasty were included in the study. All patients were imaged using the interferometer and slit lamp photography. The interferometer produces an orthogonal cross-like image of stromal lamellae by illuminating the cornea in phase-light polarization angle. Corneal haze was graded on a scale from 0 to 4. Interferometer cross-like images were graded on a scale from 0 to 5 by a masked observer. Keratometry, corneal central pachymetry, and optical densitometry were obtained with Scheimpflug corneal tomography. RESULTS: The cross-like image was observed in 31 of 32 healthy control subjects (58 of 59 eyes) and in 13 of 16 patients (19 of 22 eyes). The image was not detectable in 3 patients, 1 with total corneal neovascularization and scarring, 1 with central leukoma, and 1 with failed deep lamellar keratoplasty. Corneal haze was the main factor obscuring the cross-like image (P < 0.05). Clarity of the images was influenced by older age, steeper keratometry, higher pachymetry, and optical density (P < 0.05). There was no correlation between the orientation of the keratometric axes and the orientation of the 2 arms of the cross-like image (P > 0.05). CONCLUSIONS: Corneal stromal lamellae orientation can be efficiently assessed and displayed as a cross-like image by the polarimetric interferometer. Stromal lamellae orientation imaging may have potential implications in corneal diagnostics and surgery.
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 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 | 0.000 |
| Open science | 0.000 | 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".