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Record W2341774651

Comparison of the INNOVA Visual Acuity System Stereotest with the Frisby-Davis 2 Stereotest for the Evaluation of Distance Stereoacuity.

2013· article· en· W2341774651 on OpenAlexaboutno aff
Eric L. Singman, Noelle S. Matta, David I. Silbert, Jing Tian

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Eye Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsStereoscopic acuityOptometryMedicineStereopsisTest (biology)Visual acuityOphthalmologyOpticsPhysicsGeology
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: Distance stereo acuity has been shown to be useful in monitoring conditions such as control of intermittent strabismus. The Frisby Davis distance (FD2) stereotest has been shown to be reliable and is felt to be the gold standard in England. The device however is not widely available in the United States or Canada and is not automated. This study compares the Innova distance stereoacuity test with the Frisby Davis distance (FD2) stereotest. METHODS: Twenty-seven patients with normal acuity and a normal ophthalmology exam were evaluated. Prior to dilation all patients had an Innova distance stereoacuity test and FD2 test. Both the Innova distance stereoacuity test and the FD2 test were performed at ten feet. The results of the tests were compared using Bland-Altman plot analysis. RESULTS: The INNOVA system tended to underestimate distance stereoacuity by approximately 30 arc seconds compared to the FD2 test. If the INNOVA results were corrected by this amount, then there was a good correlation between the INNOVA results and the FD2. CONCLUSION: The Innova distance stereoacuity test underestimates stereopsis by approximately 30 arc seconds but does so with sufficient consistency that it may serve as an acceptable method of measuring distance stereoacuity. This study is the first that has correlated the Innova stereoacuity test with the FD2.

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)0.002
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 score0.095
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.081
GPT teacher head0.348
Teacher spread0.267 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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