Bibliographic record
Abstract
PURPOSE: To summarize a variety of issues associated with contact lens case contamination and discuss appropriate methods that can limit this. METHODS: A literature review was undertaken investigating the major factors associated with case contamination, with specific reference to the major pathogens associated with contamination, the role of bacterial biofilms, and methods that can limit contamination. RESULTS: The use of antimicrobial cases, regular case cleaning and case replacement, avoidance of topping up solutions, and not using tap water to rinse cases all appear to be important in avoidance of significant case contamination. CONCLUSIONS: Contact lens case contamination is a significant public health concern and may contribute significantly to the development of microbial keratitis in contact lens wearers. Patients should be reminded that they must clean and disinfect their lens cases daily, should avoid the use of tap water for rinsing them, must not top up their solutions, must take into careful consideration where and how the cases are stored during the time that lenses are being worn and that they must be replaced regularly. The adoption of these methods will substantially reduce the levels of contamination of cases with pathogenic microbes.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".