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Record W4406427393 · doi:10.22599/bioj.426

The Psychological Impact of Amblyopia Treatment: A Systematic Literature Review

2025· article· en· W4406427393 on OpenAlexaboutno aff
Louisa A Haine, Megan Vaughan

Bibliographic record

VenueBritish and Irish Orthoptic Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsnot available
FundersAnglia Ruskin University
KeywordsDistressMedicineStrabismusClinical psychologyPediatricsPsychiatryPsychologyOphthalmology

Abstract

fetched live from OpenAlex

Aim: The aim of this literature review was to determine if a consensus could be reached on whether amblyopia treatment causes distress to patients and/or their guardians, and if so, establish the impact of this reported psychological distress upon paediatric patients and/or their parents/guardians. Methods: A systematic review of the literature was conducted of all publications written in English. Search terms included both MeSH terms and alternatives related to amblyopia and psychological distress. Evidence quality was assessed using an adapted Newcastle-Ottawa Score (NOS) and evaluation of the literature was used to form a narrative synthesis of the findings. Results: Initial searches yielded 7,838 titles in total, with 25 peer reviewed papers published between 1999 and 2021 meeting the study inclusion criteria. Factors such as the presence of strabismus, moderate and severe amblyopic density, occlusive patch treatment and patching during school age increase the likelihood of experiencing distress as a result of amblyopia treatment. Conclusions: Both parents/guardians and patients can experience psychological distress as a result of undertaking amblyopia treatment. School-aged children and those receiving occlusion therapy in the form of patching report higher distress than infants and young-children, and those receiving atropine occlusion therapy or refractive correction only. Further study measuring the physiological markers of distress such as Cortisol and BDNF, is recommended.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.399
Teacher spread0.377 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2025
Admission routes1
Has abstractyes

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