Bibliographic record
Abstract
In Brief Background: Brilliant Blue G (BBG) is a safe and effective dye used to highlight the internal limiting membrane during macular surgery. The authors proposed that the utilization of a 0.22-μm filter intraoperatively can reduce the risk of inoculating an eye with contaminated BBG. Methods: An in vitro model of contaminated BBG was prepared. Laboratory stock cultures of 7 organisms including, Staphyloccocus epidermidis, Streptococcus pneumoniae, Staphyloccocus aureus, Haemophilus influenza, Klebsiella pneumoniae, Fusarium species, and Candida albicans, were prepared in five 10-fold dilutions and injected into BBG vials. These mixtures were drawn with either a 5-μm filter, 0.22-μm, or without a filter and cultured on appropriate plates and growth conditions. Results: No culture plates that had inoculate drawn through a 0.22-μm filter showed evidence of growth. There was evidence of growth for all organisms when no filter was used. A 5 μm was insufficient to filter Fusarium species. Conclusion: Using a 0.22-μm filter in the intraoperative processing of BBG would likely reduce the risk of infectious endophthalmitis resulting from contaminated dye. Brilliant Blue G is a dye used to identify the internal limiting membrane during macular surgery and has been associated with a recent outbreak of Fusarium species endophthalmitis. We present in vitro evidence that the use of a 0.22-μm filter can reduce the risk of postoperative endophthalmitis associated with the use of Brilliant Blue G.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".