Precorneal and Pre- and Postlens Tear Film Thickness Measured Indirectly with Optical Coherence Tomography
Bibliographic record
Abstract
PURPOSE: To demonstrate the feasibility of indirectly measuring the precorneal tear film thickness and pre- and postlens tear film (PLTF) thickness using optical coherence tomography (OCT). METHODS: Central corneal thickness (C(1)) which includes the tear film (T) of both eyes of 40 non-contact lens wearers was measured using OCT after calibration. The mean age of the 40 subjects was 31.2 +/- 9.3 years with a mean horizontal K-reading of 7.87 mm. Rigid contact lenses with base curves 0.3- to 0.5-mm steeper than the flattest K of the eye were fitted to measure real corneal thickness (C(2)), independently of the postlens tear film. T was calculated by T = C(1) - C(2). To measure pre- and postlens tear film thickness, Focus Night & Day and Acuvue lenses (Vistakon, Johnson & Johnson Vision Care, Jacksonville, FL) were fitted on both eyes. Central soft lens thickness (L(1)), which includes the prelens tear film (P), was measured by OCT in situ and in saline in a wet cell (L(2)). P was calculated by P = L(1) - L(2). Thickness of the central cornea plus the postlens tear film (C(3)) was measured during lens wearing. Postlens tear film (PLTF) was calculated by PLTF = C(3) - C(2). RESULTS. The mean +/- SD precorneal tear film thickness was 3.3 +/- 1.5 microm (range, 0-6.9) before lens insertion and 4.7 +/- 2.3 microm (range, 0.7-11.0) after lens fitting, which was significantly thicker (paired t-test: P < 0.01). The prelens tear film thickness was 3.9 +/- 2.6 and 3.6 +/- 2.1 microm (mean +/- SD; paired t-test: P = 0.52) and the postlens tear film thickness was 4.5 +/- 2.3 and 4.7 +/- 3.1 microm (paired t-test: P = 0.08) on and under Focus Night & Day and Acuvue lenses, respectively. Post hoc tests showed that precorneal (baseline) and prelens tear films were equivalent, and each was different (thinner; Tukey honestly significant difference P < 0.05) from the postlens tear film. CONCLUSIONS: OCT can noninvasively measure the thickness of the precorneal and prelens tear film as well as the postlens tear film. The thickness of the normal precorneal tear film is approximately 3 micro m and becomes thicker after lens fitting. The postlens tear film is thicker than the precorneal and prelens tear films with soft contact lenses. The thickness of both pre- and postlens tear films appears to be independent of the investigated lens types.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".