Bibliographic record
Abstract
The first suggestion of a disease associated with a rise in intraocular pressure (IOP) and thus corresponding to what is now known as glaucoma seems to occur in the Arabic writings of Shamms Ad-Deen of Cairo (died ad 1348) who, among 153 diseases of the eye and adnexa, described a “migraine of the eye” or “headache of the pupil,” an illness associated with pain in the eye, hemicrania, and followed by dilatation of the pupil and cataract. If it became chronic, tenseness of the eye and blindness supervened.1 Eldaly and co-authors, from Cairo University in Cairo, Egypt, the same city of Shamms Ad-Deen, produced an interesting article on the socio-economic impact among Egyptian glaucoma patients.2 Egypt, with a population close to 75 million, of whom 20% fall below the poverty line, serves as an excellent model of how glaucoma is managed in a developing country. Having said so, it is of fundamental importance to discourage the grouping of all developing countries in one basket, as circumstances vary significantly between them. This includes the number of physicians per capita, gross domestic product (GDP), and total expenditure on health as percentage of GDP, among others. Egypt has a ratio of 2.1 physicians per 1000 people, a ratio identical to that of the UK, New Zealand and Canada.3 The same ratio contrasts dramatically with ratios in Zimbabwe (0.05) and Rwanda (0.01). Egypt invests 6% of its GDP on health services, amounting to $258 per capita, which again can be compared with the UK (8%, $2560 per capita) and Rwanda (3.7%, $14 per capita).4 The statistics cannot …
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".