Clinical Significance of Central Corneal Thickness in the Managementof Glaucoma
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of central corneal thickness determination on the clinical management of patients with glaucoma and glaucoma suspect. METHODS: A cross-sectional retrospective study was performed on 188 consecutive patients. Mean ultrasound pachymetry measurements of central corneal thickness and corresponding Goldmann applanation tonometry measurements were obtained. Intraocular pressures (IOPs) were corrected using linear and mathematical (Orssengo-Pye) algorithms. Measurement-significant outcomes were defined as an IOP adjustment of 1.5 mm Hg or greater and outcomes-significant results as an IOP adjustment of 3.0 mm Hg or greater. Changes in therapy such as the use of eyedrops and addition or cancellation of laser therapy or surgery were then noted for those individuals with measurement- or outcomes-significant changes. RESULTS: Using the linear correction scale, 105 (55.9%) of 188 patients had at least a measurement-significant adjustment in their IOP measurements: 67 (35.6%) had adjustments between 1.5 and 3.0 mm Hg, while 38 (20.2%) had an outcomes-significant IOP adjustment (> or =3.0 mm Hg). Among the 188 patients, 16 (8.5%) had a change in eyedrop therapy, 4 (2.1%) had a change regarding laser therapy, and 6 (3.2%) had a change in the decision regarding glaucoma surgery. Using the exponential correction (Orssengo-Pye) scale, similar percentages were obtained. CONCLUSION: Pachymetry-measured central corneal thickness has a significant effect on the clinical management of patients with glaucoma and glaucoma suspect.
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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 | 0.016 |
| 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) | 0.001 | 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".