Retinal ultra-wide-field colour imaging versus dilated fundus examination to screen for sickle cell retinopathy
Bibliographic record
Abstract
PURPOSE: To compare ultra-wide-field colour fundus imaging (UWFI) to dilated fundus examination (DFE) for the screening of sickle cell retinopathy (SCR). DESIGN: This study is a prospective, blinded, multicentre case series. PARTICIPANTS: This study included two groups: an adult group (n=268 eyes) and a paediatric group (n=168 eyes). Sickle cell disease (SCD) types included haemoglobin S homozygous (HbSS), haemoglobin S and C (HbSC) and Hb S with β-thalassaemia (HbSß-Thal). METHODS: Participants underwent DFE and UWFI. Each eye received three independent grades (1-4), documented by three graders: clinical grader, image grader 1 and image grader 2. Three clinically relevant diagnostic thresholds were determined. Based on these thresholds, the sensitivity, specificity, positive predictive value and negative predictive value for all three graders were calculated relative to each other as reference tests. RESULTS: HbSC was associated with the most advanced SCR grades. When compared to the clinical grader, image grader 1 and image grader 2 consistently detected more SCR and higher SCR grades in both adult and paediatric groups. In both groups, image grader 1 and image grader 2 identified twice as many cases of capillary occlusion/anastomosis than clinical grader. To detect the presence of any proliferative SCR, image grader 1 and image grader 2 had a sensitivity of 82%, 71% in the paediatrics group and 90% and 72% in the adult group. The clinical grader sensitivity was 52% in the paediatrics group and 53% in the adult group. CONCLUSION: The UWFI is a sensitive tool to screen for SCR. It is superior to DFE in detecting capillary occlusion or anastomosis.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".