Enhancing Strabismus Education through Interactive Learning Multimedia
Bibliographic record
Abstract
The objective of our project was to create an interactive online educational module that integrates biomechanics of the eye with its underlying anatomy to allow for a comprehensive understanding of pediatric strabismus and its clinical detection to prevent amblyopia. Amblyopia is the leading cause of monocular vision loss in the pediatric population, affecting 3–5% of children. Untreated, it leads to irreversible visual impairment in an otherwise structurally normal eye and can have significant effects on quality of life that impact self‐esteem and restrict career opportunities due to loss of stereopsis. Majority of cases are caused by strabismus, a condition resulting in the ocular misalignment of the eye. Early intervention significantly improves vision outcomes and psychosocial well‐being of the patient, however requires prompt and skilled detection. The pediatric eye examination can be inherently challenging, particularly considering the low emphasis on ophthalmology teaching in medical school curricula. Primary care residents and undergraduate medical student trainees often report low levels of comfort performing screening eye examinations and managing common pediatric ophthalmological presentations. Integration of open educational resources such as interactive clinical skills modules can effectively advance ophthalmology education and promote prompt detection of pediatric strabismus, with the goal of mitigating serious ophthalmic complications and improving overall patient care. To effectively address these educational challenges, we have developed an innovative online module that correlates ocular anatomy to biomechanical mechanisms, and provides practical guidelines to clinical examination. The module focuses on the pediatric eye examination, evaluating for amblyopia & strabismus, and was created through an international collaboration with experts in anatomy, education, and neuro‐ophthalmology to optimize content delivery and integrate clinical relevance. It offers a variety of interactive activities to engage student learning and concludes with a set of virtual cases entailing simulated clinical examination to assess acquired knowledge. Critical feedback will be collected using a student experience survey at the beginning and end of the module to solicit response regarding effectiveness of content delivery, preference of educational media, and comfort conducting a strabismus clinical examination. This international effort aims to significantly improve confidence performing clinical examination, guide prompt amblyopia detection in the pediatric population, and prevent development of its irrevocable complications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".