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Record W2106610076 · doi:10.3368/aoj.57.1.89

Stability of Visual Acuity after the Cessation of Amblyopia Treatment: Review of the Literature

2007· article· en· W2106610076 on OpenAlexaff
Leah A. Walsh, Erik K. Hahn, Gaétan Laroche

Bibliographic record

VenueAmerican Orthoptic Journal · 2007
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsIzaak Walton Killam Health Centre
Fundersnot available
KeywordsMedicineVisual acuityOptometryOphthalmology

Abstract

fetched live from OpenAlex

INTRODUCTION AND PURPOSE: The treatment of amblyopia in children is frequently discussed in the literature. Less attention, however, has been given to the durability of the visual acuity results attained with therapy. The objective of this review is to conduct an in-depth analysis of the existing literature, on the stability of visual acuity following cessation of amblyopia treatment, and to identify any gaps in the literature, which could guide future investigations. RESULTS: There did not appear to be any one consistent risk factor affecting the stability of vision after cessation of amblyopia treatment. Most of the reviewed studies varied with respect to lengths of follow-up visits, patient population, and method of visual acuity assessment. There was also a generalized lack of standardization of visual acuity measurements in these previous investigations. Only one of the studies analyzed was a prospective design. CONCLUSION: The area of study in amblyopia is fraught with contradictions. It is obvious from this review that there exists uncertainty regarding the recurrence of amblyopia following treatment. Previous studies have failed to identify any common, predictive, influencing factors necessary for the maintenance of visual acuity after cessation of therapy. Also lacking is discussion on the potential role that therapy tapering plays in the recurrence of amblyopia following the cessation of treatment.

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)0.001
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 score0.014
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.361
Teacher spread0.342 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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