Keratoconus and crosslinking: pharmacokinetic considerations
Bibliographic record
Abstract
INTRODUCTION: Keratoconus (KC) is an ectatic disorder characterized by the progressive thinning and scarring of central cornea as a consequence of structural and/or compositional anomalies, although the exact etiology is largely unknown. The resultant conical protrusion of the cornea causes significant irregular astigmatism, myopia and visual impairment. AREAS COVERED: This paper will review the biochemical factors involved in the multifactorial pathogenesis of KC and the possible emerging role of inflammation in this process. Additionally, the authors discuss the development of new corneal collagen crosslinking (CCL) protocols using hypoosmolar riboflavin or transepithelial approaches with respect to the associated toxicities at the cellular level. EXPERT OPINION: Long-term consequences of standard and new emerging CCL protocols are still being studied; hence, pharmacokinetic considerations and related potential toxicities are important to consider. CCL sequentially combined with other modalities, specifically intrastromal corneal ring segments and photorefractive keratectomy can optimize the visual rehabilitation of KC patients. As we come to further understand CCL and its pharmacokinetic effects, additional indications for CCL may be discovered especially for those patients who are not suitable candidates for keratoplasty. Randomized control trials evaluating the efficacy of CCL for applications beyond halting KC disease progression are warranted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".