[Analysis of the eye's anterior segment with optical coherence tomography. Static and dynamic study].
Bibliographic record
Abstract
PURPOSE To study the biometric modifications of the anterior segment depending on accommodation and age. To try and define their possible applications in certain fields of anterior segment surgery, in particular in refractive implants. MATERIAL AND METHOD Anterior chamber biometry can be very easily studied with 1310-nm wavelength optical coherence tomography. The equipment has a fixation target that can be focused and defocused with negative lenses in order to stimulate natural accommodation. The human anterior chamber was therefore studied during accommodation. We studied 104 eyes of 56 patients aged between 7 and 82 years. Refraction was between +5D and - 5D. A single operator carried out all the measurements. The anterior chamber's horizontal diameter, the anterior chamber's depth, the horizontal pupil diameter and the horizontal radius of curvature of the crystalline lens' anterior pole were measured unaccommodated or after stimulating accommodation. RESULTS The different static or dynamic measurements were compared to ametropia, age and accommodation. At rest, the average AC diameter was 12.33 mm, the average AC depth was 3.11 mm and the average pupil diameter was 4.26 mm. On average, for 1 D of accommodation, the crystalline lens anterior pole moved forward by 30 microm. There was a 0.3-mm reduction in its radius of curvature and a 0.15-mm reduction in pupil diameter. Several other measurements are illustrated on graphs. CONCLUSIONS The AC OCT is a user-friendly instrument to evaluate the anterior segment and explore the anterior chamber (cornea, iris, crystalline lens, irido-corneal angle). The 1310-nm light wavelength is blocked by pigments preventing exploration behind the iris. However, the AC OCT is capable of providing good-quality images and a better visualization of the anatomical relationships of the anterior segment, even behind an opaque cornea.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".