Eye clinic liaison officers service in the United Kingdom
Bibliographic record
Abstract
BACKGROUND: To investigate the role of eye clinic liaison officers (ECLOs) in the United Kingdom and analyse patients' demographics and services provided. METHODS: This is a retrospective observational study. Data were collected from the Royal National Institute of Blind People for ECLOs in Wales, Scotland, Northern Ireland and England for the first quarter of 2015. Statistical analysis was performed using chi-square and t test as appropriate. RESULTS: Trusts with ECLOs support vary greatly in the UK regions. Only one-third of NHS trusts in England have an ECLO service. Over 4000 patients were assessed. The majority of patients were of White ethnic background (94%), lived alone (37%), had no carers (58%) and were in their 80s (29.5%). The principal ocular conditions causing sight loss and certification were age-related macular degeneration (41.6%) and glaucoma (18.1%). Approximately 70% of patients are first seen at 13 to 18 months from diagnosis. CONCLUSIONS: ECLO services vary in the UK regions. England has the lowest ECLO availability per trust and the majority of those assessed were of White British origin with AMD. There are significant delays from diagnosis to the first visit indicating the need for improved services. Further studies are necessary to develop the evidence base for the expansion and funding of ECLO services.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".