Screening attendance, age group and diabetic retinopathy level at first screen
Bibliographic record
Abstract
AIMS: To report on the relationships between age at diagnosis of diabetes, time from registration with the screening programme to first diabetic eye screening and severity of diabetic retinopathy. METHODS: Data were extracted from four English screening programmes and from the Scottish, Welsh and Northern Irish programmes. Time from diagnosis of diabetes to first screening and age at diagnosis were calculated. RESULTS: Time from registration with the screening programme to first screening episode is strongly related to age at registration. Within 18 months of registration 89% of 3958 young people under 18 years of age and 81% of 391 293 people over 35 years of age were seen. In 19 058 people between 18 and 34 years of age, 80% coverage was not reached until 2 years and 9 months. The time from diagnosis of diabetes to first screening is positively associated with severity of disease (P < 0.0001). CONCLUSIONS: This report is the first that to demonstrate that those in the 18-34 year age group are least likely to attend promptly for screening after registration with a higher risk of referable diabetic retinopathy being present at the time of first screen. Date of diagnosis should be recorded and prodigious efforts made to screen all people promptly after diagnosis. Screening programmes should collect data on those who have not attended within one year of registration.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".