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Record W4323063424 · doi:10.22259/2638-5120.0401001

Face and Content Validity of an Artificial Eye Model for Secondary IOL Fixation

2021· article· en· W4323063424 on OpenAlexaffabout
David Loewen, Abdullah Al-Ani, Michael Penny, Andrew J. Swift, Adam Gorner, Patrick Gooi

Bibliographic record

VenueArchives of Ophthalmology and Optometry · 2021
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFixation (population genetics)Content validityFace validityFace (sociological concept)Computer scienceOptometryArtificial intelligencePsychologyBiologyMedicineDevelopmental psychologySociologyPsychometrics

Abstract

fetched live from OpenAlex

Purpose: To evaluate the face and content validity of the use of artificial eye models (SimulEYE Iris Suturing & IOL model, InsEYEt, Westlake Village, CA) in training physicians the techniques required for secondary intraocular lens (IOL) fixation.Secondary IOL fixation, or secondary IOL implantation, is a common yet technically challenging skill useful in the treatment of primary IOL implantation failure, zonular instability, or aphakia related to trauma or surgery. Materials and Methods:Twenty-eight ophthalmologists at the 2019 Canadian Ophthalmology Society annual meeting underwent a secondary IOL fixation wet-lab using artificial eye models.The technique was scleral suturing of a single piece IOL with via 4 closed-loop haptics.All of the ophthalmologists were given an 18-response survey immediately following the training session which were composed of statements which addressed the face and content validity of the artificial eyes.Responses were recorded on a 5-point Likert-type scale ranging from (5) strongly agree to (1) strongly disagree.Mann-Whitney U analysis compared instructor versus non-instructor and expert versus non-expert respondent responses. Results: Respondents rated all statements regarding the model with a median response of 3 (Neither Agree or Disagree) to 5 (Strongly Agree). Mann-Whitney U analysis did not show a significant difference in responses for expert versus non-expert and instructor vs non-instructor respondents for any of the survey statements.The artificial eye received highest ratings for its usefulness for training residents, ease of set-up and clean-up compared to cadaveric models and how using the model is a better way to learn the procedure than discussion or observation and will likely result in future surgical success.The lowest survey ratings were for the model's realism when compared to human cadaveric models. Conclusions:The artificial eye model was generally regarded highly by ophthalmologists with all levels of experience performing secondary IOL fixation.These results suggest that this artificial eye may be a valuable tool for teaching secondary IOL fixation in competency-based ophthalmology education programs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.093
GPT teacher head0.353
Teacher spread0.259 · 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
Published2021
Admission routes2
Has abstractyes

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