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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".