SEASONAL VARIATION IN THE INCIDENCE OF RHEGMATOGENOUS RETINAL DETACHMENT WORLDWIDE
Bibliographic record
Abstract
PURPOSE: To assess the variability in the incidence of rhegmatogenous retinal detachment (RRD) across seasons. METHODS: The study protocol was registered in PROSPERO (CRD42022378855), and the study was conducted in adherence to the Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines. A detailed search was conducted of Ovid MEDLINE, Ovid EMBASE, Cochrane Central Register of Controlled Trials, and CINAHL. Two independent reviewers (P.M.F., M.I.) performed the study selection, data extraction, and quality assessment. RESULTS: The 18 included studies identified 384,723 cases of RRD across North America, Europe, Asia, and Australia. 12/18 (66%) included studies including 366,219 cases (95%) observed a seasonal variation in the incidence of RRD, with higher numbers of cases in summer and spring. Of all meteorological variables, low atmospheric pressure and high solar radiation may be associated with higher RRD incidence. CONCLUSION: Epidemiologic studies suggest a seasonal variation in RRD incidence, with more cases observed during spring and summer months. This observation may correlate with periods of elevated solar radiation and reduced atmospheric pressure. These associations do not imply causation. Large population-based studies are required to verify the associations identified in the present systematic review.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".