Cascade screening for glaucoma in high-risk family members of African-Caribbean glaucoma patients in an urban population in London
Bibliographic record
Abstract
BACKGROUND/AIMS: Cascade screening has been used successfully in relatives of patients with inherited cancers and other genetic diseases to identify presymptomatic disease. This study was designed to examine if this approach would be successful in a high-risk group: first-degree relatives (FDR) of African-Caribbean glaucoma patients resident in London. METHODS: African-Caribbean patients (probands) with glaucoma from an inner London hospital setting in a deprived area were asked to disseminate personalised information to their FDR over the age of 30 and to arrange a free hospital-based screening. Data collected, including optical coherence tomography imaging, were reviewed by a glaucoma specialist and if glaucoma was diagnosed or suspected, local specialist referral via family doctor was made. RESULTS: 203 probands were recruited from glaucoma clinics. 248 suitable FDR were identified as potentially eligible to attend screening. 57 (23%) FDR made contact with the research team of whom 18 (7%) attended a subsequent screening visit. No patients were diagnosed with glaucoma; one participant was diagnosed as glaucoma suspect. Reasons for poor uptake included reluctance by probands to involve their family members, and retirees spending significant time abroad. CONCLUSION: Cascade screening of FDR of African-Caribbean glaucoma patients in inner city London was unsuccessful. Research confidentiality guidance prohibiting research teams directly contacting family members was a barrier. Greater community engagement, community-based screening and permission to contact FDR directly might have improved uptake.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".