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Record W2604355783 · doi:10.1080/2576117x.2019.1607428

A Comparison of the Maximum Deviation Measured in Intermittent Exotropia Using Various Clinical Conditions

2019· article· en· W2604355783 on OpenAlexaff
Kailee Algee, Leah Walsh, Erik K. Hahn

Bibliographic record

VenueJournal of Binocular Vision and Ocular Motility · 2019
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Eye Disorders
Canadian institutionsDalhousie University
Fundersnot available
KeywordsIntermittent exotropiaExotropiaSignificant differenceMean differenceOphthalmologyFixation (population genetics)Standard deviationClinical significancePopulationMedicineStrabismusSurgeryInternal medicineMathematicsStatisticsConfidence interval

Abstract

fetched live from OpenAlex

Background and Purpose: In the Intermittent Exotropia (IXT) population determining the largest deviation for surgical planning has been suggested for desired surgical outcomes Throughout the literature, the clinical tests that elicit largest deviation remains unclear.Patients and Methods: 24 IXT subjects were measured at the customary 1/3 m and 6 m fixation, with +3D lenses at 1/3 m, at far distance (20 m), 1/3 m and 6 m after PMO, with +3D lenses at 1/3 m after PMO, and far distance (20 m) after PMO, in an attempt to determine which of these conditions elicit the largest exodeviation.Results: At near, all subjects had clinically significant increases with at least one condition. In 87.5%, clinical and statistical increases occurred with +3D lenses and/or with +3D after PMO. There was no statistically significant difference between those conditions. At distance, 16.7% demonstrated clinically significant increases. Two increased at 20 m and 6 m after PMO similarly, and all increased at 20 m fixation with or without PMO, without a significant difference between measurements at 20 m and 20 m after PMO. All increases at 20m, with and without PMO were statistically significant.Conclusion: This research indicates that measurements with +3D lenses and at 20 m are the most efficient for the maximum deviation in IXT patients.

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 score0.005
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.059
GPT teacher head0.424
Teacher spread0.365 · 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
Published2019
Admission routes1
Has abstractyes

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