The Prevalence and Causes of Visual Loss Among HIV-Infected Individuals in Uganda
Bibliographic record
Abstract
AIM: To determine the prevalence of loss of visual acuity and to describe the ocular diseases associated with vision loss among HIV-infected individuals in Uganda.Methods: One thousand two hundred twelve HIV-positive individuals aged 18 years or older attending an HIV treatment site in Kampala,Uganda, were consecutively screened for loss of visual acuity using a Snellen chart. Those found to have a visual acuity of 6/9 or less in 1 or both eyes had a detailed ocular diagnostic evaluation.Results: One hundred thirty-six patients [11.2%; 95% confidence interval (CI): 9.49–13.13] had a visual acuity of 6/9 or less in at least 1 eye, with 74 (6.1%; 95% CI: 8.54–12.21) having bilaterally reduced presenting visual acuity. Eighty-eight (7.3%; 95% CI: 8.57–12.28)had a visual acuity of 6/18 or worse in at least 1 eye. Ocular diseases associated with reduced vision included cataract 16 (11.8%), optic nerve disease 20 (14.7%), refractive errors 35 (24.3%), and uveitis 44 (32.3%). Other diagnoses observed included diabetic retinopathy,maculopathies, corneal scars, glaucoma, and squamous cell carcinoma of the conjunctiva.Conclusions: Visual impairment and ocular disease affect a large proportion of HIV-infected individuals presenting for HIV care in Uganda. Most causes of vision loss were treatable or could have been prevented with appropriate ophthalmic and medical care.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".