The Analysis of corneal transplantation in the Hospital of Lithuanian University of Health Sciences Eye Clinic over 6 years
Bibliographic record
Abstract
Introduction:Corneal blindness account for 4% of the world’s 45 million blinds, a large portion of which is treatable [1]. Blindness due to corneal disease results from numerous degenerative, dystrophic, infectious, and inflammatory corneal disorders and corneal damage appears secondary to ocular surface disease [2]. Treatment for many people with these conditions can be provided via a corneal transplantation (CT), making access to CT essential to prevent blindness and subsequent disability [3]. CT is considered the world’s most frequent type of transplantation: nearly 200 000 transplantations a year are performed in 116 countries cooperating with 742 eye banks. The United States has the highest transplantation rate, followed by Lebanon and Canada. About 53% of the world’s population have no access to corneal transplantation [4]. 30-70 CT are performed in Lithuania each year, mostly in the Hospital of Lithuanian University of Health Sciences Kauno klinikos, with around 100 patients on the corneal transplant waiting list [5]. Aim: to analyze indications and surgical techniques of corneal transplantation at the Hospital of Lithuanian University of Health Sciences Eye Clinic over a 6 year period. Methodology: the retrospective analysis has been done after collecting data from the National Transplant Bureau [5] and health records of all the patients (n=125) who underwent corneal transplantation surgery at the Eye Clinic of Lithuanian University of Health Sciences during 2010-2015. The significance level of p=0.05 was chosen to test statistical hypotheses. Results:during this 6 year period from a total of 125 corneal transplants performed, we had access to 114 medical records (91.2%). [...].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".