Scleral Lens Issues and Complications Related to Handling, Care and Compliance
Bibliographic record
Abstract
Scleral contact lens (ScCL) handling may be challenging and is the principle reason for ScCL drop out. ScCL care systems are more intricate than other lens modalities and include solutions for cleaning, disinfection, storing, rinsing and filling the lens; respecting the use of each solution recommended is fundamental. Replacement of the lenses, solutions, case and plungers are important in order to decrease the risk of adverse events associated with ScCL wear. Compliance is crucial regarding hygiene, solution use, case and plunger care, wear time, follow-up schedule, and handling techniques. Non-compliance may lead to discontinuation of ScCL due to difficulties associated with this unique lens design. This paper presents complications secondary to handling, care and compliance that clinicians and patients may encounter while wearing ScCL. Instructions are provided to enhance the understanding on management surrounding these issues. This manuscript includes three tables to summarize types of complications, their symptoms, clinical signs, etiology, and management for a quick find index for easy consultation during daily clinical practice.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".