Why do people present late with advanced glaucoma? A qualitative interview study
Bibliographic record
Abstract
OBJECTIVE: To explore the presentation behaviours and pathways to detection of adults who first presented to UK hospital eye services with severe glaucoma. DESIGN: Semistructured interviews, based on models of diagnostic delay, to obtain a descriptive self-reported account of when and how participants' glaucoma was detected. RESULTS: 11 patients participated (five in Aberdeen, six in Huddersfield). Four participants reported that the optometry appointment at which their glaucoma was detected was their first ever eye test or their first for over 10 years. Seven participants reported attending regular routine optometrist appointments. Their self-reported experiences and pathways to detection describe a variety of missed detection opportunities and delayed referral and treatment. CONCLUSIONS: The qualitative data suggest that late detection of glaucoma can result from delays at the patient level but, although based on a small sample, delays also occurred at the healthcare provider (system) level both in terms of accuracy of case detection and effective referral. We suggest that current attempts to address the significant burden of over-referral of glaucoma suspects to hospital eye services (a large proportion of which are false positives) must also focus on the issue of false negatives and on reducing missed detection and service delays.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".