Philadelphia Telemedicine Glaucoma Detection and Follow-Up Study: Cataract Classifications Following Eye Screening
Bibliographic record
Abstract
Background: Cataracts are a major cause of visual impairment and blindness in the United States and worldwide. Introduction: Risk factors for cataracts include age over 40 years, smoking, diabetes, low socioeconomic status, female sex, steroid use, ocular trauma, genetic factors, and exposure to ultraviolet-B light. Community-based telemedicine vision screenings can be an efficient method for detecting cataracts in underserved populations. The Philadelphia Telemedicine Glaucoma Detection and Follow-Up Study reports the prevalence and risk factors for cataracts in individuals screened and examined for glaucoma and other eye diseases. Materials and Methods: A total of 906 high-risk individuals were screened for glaucoma using telemedicine in seven primary care practices and four Federally Qualified Health Centers in Philadelphia. Participants with suspicious nerves or other abnormalities on fundus photographs, unreadable images, and ocular hypertension returned for an eye examination with an ophthalmologist at the same community location. Results: Of the participants screened through telemedicine, 347 (38.3%) completed a follow-up eye examination by an ophthalmologist. Of these, 267 (76.9%) were diagnosed with cataracts, of which 38 (14.2%) had visually significant cataracts. Participants who were diagnosed with visually significant cataract were more likely to be older ( p < 0.001), have diabetes ( p = 0.003), and worse visual acuity ( p < 0.001). Discussion: Our study successfully detected and confirmed cataracts in a targeted, underserved urban population at high risk for eye disease. Conclusions: Telemedicine programs offer an opportunity to identify and refer individuals who would benefit from continuous follow-up eye care and treatment to improve visual function and quality of life.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".