Human Albumin Eye Drops as a Therapeutic Option for the Mmanagement of Keratoconjunctivitis Sicca Secondary to Chronic Graft-Versus-Host Disease after Stem-Cell Allografting
Bibliographic record
Abstract
BACKGROUND: Keratoconjunctivitis sicca from chronic graft-versus-host disease (cgvhd) after allogeneic stem cell transplantation is common, leading to severe corneal damage and blindness if not treated. We retrospectively examined the efficacy and safety of pooled human albumin eye drops (haeds) for symptom relief in 40 stem-cell transplantation patients after other alternatives had failed. METHODS: The Common Terminology Criteria for Adverse Events (version 4.0) and the cgvhd grading scale were used to compare response in the patients during January 2000 and July 2013. In addition, on days 1 and 30, the haeds were subjected to quality assurance testing for sterility, oncotic pressure, albumin measurement, viscosity, pH, and purity by protein electrophoresis. RESULTS: Use of haeds resulted in symptom relief for 37 patients (92.5%); 3 patients (7.5%) failed to improve with use of haeds (p ≤ 0.0001). Of the 37 patients having symptom relief, 7 (19%) improved from grade 3 to no dry eye symptoms. Proportionately, post-treatment symptom improvement by two grade levels, from 3 to 1 (70%), was significantly higher than improvement by one grade level, from 3 to 2 (11%) or from 2 to 1 (19%, p ≤ 0.0001). Time to symptom relief ranged from 2 weeks to 28 weeks. Of the 40 patients, 38 (95%) had no adverse reactions. Days 1 and 30 quality assurance testing results were equivalent. CONCLUSIONS: Complications of keratoconjunctivitis sicca were well managed and well tolerated with haeds when other remedies failed. Quality assurance testing confirmed that haeds were safe and stable in extreme conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".