Face and Content Validity of an Artificial Eye Model for Secondary Intraocular Lens Fixation via Yamane Technique
Bibliographic record
Abstract
Purpose: To assess the face and content validity of an artificial eye model for secondary intraocular lens (IOL) fixation via the Yamane technique. Methods: Ophthalmologists and residents participated in a 90-minute simulation session on secondary IOL fixation via the Yamane technique. Hands-on practice of this technique was performed on an artificial eye, the Bioniko Okulo BR8. After, all ophthalmologists answered an 18-question survey assessing the face and content validity of the model. Survey responses were recorded on a 5-point double-headed Likert scale, ranging from strongly agree (1)-to-strongly disagree (5) (Figure 1). Results: Twenty-three surveys were completed. Respondents rated the survey with a median response of 1 (strongly agree)-to-3 (neutral). Highest ratings for the model were received for "usefulness for training residents", and "easier to set up and clean-up compared to a cadaver". Lowest ratings were received for realism of the model compared to cadaveric eyes. Statistical analysis revealed no significant difference among identified groups. Ratings for face and content validity were viewed favorably, both with an overall median response of 2.00 (agree). Conclusion: The Bioniko Okulo BR8 shows promise as a valid tool for practicing secondary IOL fixation via the Yamane technique. Considering recent guidelines in competency-based ophthalmology education programs, this model may be a valuable tool over traditional techniques for teaching and improving surgical skill amongst trainees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".