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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".