Bibliographic record
Abstract
Allergic eye disease is extremely common as the eye is sensitive to irritants due to its constant exposure to the external environment. Approximately 40% of the general population is affected by ocular allergies. The majority of patients may also suffer with additional associated symptoms of allergic rhinitis, such as nasal congestion, sneeze, etc.; however, 6% may have isolated ocular symptoms. In addition, there are links between ocular allergies and other allergic conditions such as asthma, food allergy and atopic dermatitis. The challenge is that in addition to ocular symptoms, patients experience a substantial negative influence on their quality of life (QOL). The most common symptoms are watery and itchy eyes; redness; soreness; stinging; burning sensations; and swelling. Unfortunately, as these symptoms are quite common, most patients may choose to self-medicate and many cases are undiagnosed or underdiagnosed. As a result of this, patients may not utilize the correct management strategy; this can lead to a further propagation of symptoms and a greater reduction in patients’ QOL. Hence, it is crucial for patients to seek professional medical attention, while physicians must gather a comprehensive medical history and conduct relevant investigations. Additionally, the physician ought to propose the correct diagnosis and suitable treatment plan.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.007 | 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".