Systematic review of the association between Alzheimer’s disease and chronic glaucoma
Bibliographic record
Abstract
A potential association between Alzheimer's disease (AD) and chronic glaucoma has been suggested but results of epidemiological studies have been inconsistent. Therefore, we performed a systematic review and critical appraisal of this literature. We searched systematically in PubMed from December 1964 to September 2013 and identified 239 articles potentially relevant for abstract and full-text review. Statistical heterogeneity (variability) across studies was evaluated using the Cochran Q test and the I (2) statistic, and the Newcastle-Ottawa score was used to assess study quality. Ten studies were finally selected. Compared to non-demented participants, patients with AD had a statistically significant decreased risk of glaucoma but the results were very heterogeneous, and thus summary estimates were not reported (I (2), 89%; P heterogeneity, <0.001). The study results ranged from large positive relative risks identified in small and poorly-conducted studies to weak inverse associations or null estimates observed in some cohort and record-linkage studies, but the summary estimates were essentially driven by a large retrospective cohort using medical claims that may be afflicted by underdiagnosis bias. There was also evidence for substantial publication bias (Egger's P≤0.01). The association of AD and glaucoma is heterogeneous and most studies are small and inadequately designed. Large prospective studies with long follow-ups are warranted to clarify this association.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".