Sickle cell disease
Bibliographic record
Abstract
World Sickle Cell Awareness Day is celebrated every June 19 to raise awareness for sickle cell disease (SCD). Access to health services remains unequitable in countries affected by the disease and stigma surrounding patients hinders access to therapies. SCD is the most common severe monogenic disease in the world and is characterized by abnormal hemoglobin production. Major complications include vaso-occlusive events, hemolytic anemia, and inflammation. Microvascular events in the eye are namely responsible for sickle cell retinopathy with or without vasoproliferative changes. Methods included the electronic search of peer-reviewed English literature published until 2021, which was screened, appraised in full version, and incorporated into the review as deemed necessary. This review provides a summary of disease mechanisms and ocular manifestations, and highlights the importance of early diagnosis, close management with imaging technology, and therapeutic avenues for patients with SCD. In addition to significant healthcare barriers encountered by patients and their families, early diagnosis for SCD must be posed by physicians. It is crucial for the healthcare community to become better familiarized with the disease manifestations for early recognition and prevention of chronic complications and morbidity.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".