Bibliographic record
Abstract
AIM: To establish the epidemiology of the grey crescent in a white population within the age range most susceptible to glaucoma. METHODS: Bruce Shields was first to use this term to describe a localised, physiological pigmentation of the optic nerve neuroretinal rim tissue that is distinct from peripapillary pigmentation. An experienced glaucomatologist (KFD) evaluated stereofundus photographs of the participants of the Reykjavik Eye Study (RES)-a random sample from the national population census including people 50 years and older. 1012 right eyes could be evaluated for grey crescent. RESULTS: The prevalence of grey crescent in the right eyes was 22.0% (95% CI 10 to 25). It was more commonly found in women (27.0%: 95% CI 23 to 30) than in men (17.0%: 95% CI 14 to 21), and was most often located temporally (36.9%), 360 degrees (15.9%), or nasally (15.4%). The spherical equivalent was +1.30 dioptres (D) for those with and +0.80 D for those without grey crescent (p = 0.002), respectively. Vertical optic disc diameters were 0.203 v 0.195 units (p<0.001). There was no difference in the prevalence of grey crescent in glaucomatous or non-glaucomatous eyes (OR = 1.05, 95% CI 0.49 to 2.26). The prevalence of a grey crescent was inversely related to the prevalence of peripapillary atrophy (p = 0.001). CONCLUSIONS: The grey crescent needs to be recognised as a physiological variant in order to avoid falsely labelling eyes as having glaucomatous optic nerve damage.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".