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Record W4390141036 · doi:10.15419/bmrat.v10i11.842

Association Between Anisometropia and Amblyopia: A Systematic Review and Meta-analysis Study

2023· review· en· W4390141036 on OpenAlexaboutno aff
Fatemeh Eslami, Tahereh Mohammadi, Salman Khazaei

Bibliographic record

VenueBiomedical Research and Therapy · 2023
Typereview
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsnot available
FundersHamedan University of Medical SciencesHamadan University of Medical Sciences
KeywordsAnisometropiaMeta-analysisMedicineRefractive errorObservational studyDioptreOptometryOphthalmologyVisual acuityInternal medicine

Abstract

fetched live from OpenAlex

Background: Anisometropia is a common refractive error. It has been associated with an increased risk of developing amblyopia, a condition that can lead to permanent vision loss if left untreated. This study aimed to systematically review and pool the available evidence on the relationship between anisometropia and amblyopia. Methods: A systematic review and meta-analysis was conducted following the PRISMA guidelines. Three main databases were searched for observational studies that addressed the association between anisometropia and the risk of developing amblyopia. The quality of the included studies was assessed using the Newcastle–Ottawa scale. Results: A total of 14 studies were included in the meta-analysis, with a combined sample size of 6,895 participants. Patients with any refractive error had a higher risk of developing amblyopia compared to those without refractive errors (P<0.05). However, the risk of developing amblyopia in patients with refractive errors of less than 1 diopter was relatively small (OR: 1.66, 95% CI: 1.2, 2.12). Conclusion: This systematic review and meta-analysis provide evidence of a significant association between anisometropia and the risk of developing amblyopia. This highlights the importance of early detection and treatment of anisometropia as a potential strategy for preventing amblyopia.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.539
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.503
GPT teacher head0.578
Teacher spread0.075 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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