Strabismus and amblyopia in Africa – a systematic review and meta-analysis
Bibliographic record
Abstract
The objective of this review was to estimate the prevalence of strabismus and amblyopia in Africa. A systematic online literature search was conducted for articles on strabismus and amblyopia in Africa. Meta-analysis was performed, using the Freeman-Tukey double arcsine transformation, to estimate the prevalence of strabismus and amblyopia in Africa. Subgroup analyses were conducted according to age, gender, study year, and type of amblyopia. Meta-regression was used to evaluate the influence of predetermined factors on the prevalence of amblyopia. 8 (1 population-based & 7 school-based) and 21 (3 population-based & 18 school-based) studies on strabismus and amblyopia with sample sizes of 22,355 and 46,841, respectively, were included in the review. Overall prevalence of strabismus in Africa was estimated to be 0.8% (95% CI: 0.4% - 1.4%); exotropia was 0.2% (95% CI: 0.1% - 0.5%) and esotropia was 0.5% (95% CI: 0.1% - 1.2%). Overall prevalence of amblyopia was estimated to be 0.6% (95% CI: 0.3% - 0.9%); refractive and strabismic amblyopia were 1.1% (95% CI: 0.2% - 2.5%) and 0.4% (95% CI: 0.2% - 0.6%), respectively. Prevalence estimate of amblyopia in males was 1.8% (95% CI: 0.7% - 3.3%) and in females was 1.3% (95% CI: 0.4% - 2.6%). There was a significant association between the prevalence of amblyopia and the type of amblyopia (p = .007) and the study year (p = .006). Although there appears to be a relatively low prevalence of strabismus and amblyopia in Africa, there is a dearth of well-designed population-based studies on strabismus and amblyopia in Africa, resulting in the lack of epidemiological information on strabismus and amblyopia within the general African population. Information about the prevalence of strabismus and amblyopia across Africa can inform policy making and design and implementation of public health intervention program.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".