An Evaluation of Educational Neurological Eye Movement Disorder Video Posted on Internet Video Sharing Sites: Comment
Bibliographic record
Abstract
We found the article by Hickman (1) both enlightening and frightening. To find that less than one-quarter of the eye movement videos posted on internet video sharing websites contained excellent educational value is disturbing. Although there is some reassurance in the statistically significant finding that a greater number of “likes” was found on higher-quality videos, this is not a practical or reliable screening technique for the clinician working through a busy clinic. As pointed out by Hossain et al (2), in the United States, we are experiencing a steady decline in ophthalmic education in medical schools (3). Although many of us in academic medicine are working to re-engage in the curriculum within our respective medical schools, there is still a need for improving the access and availability of peer-reviewed online ophthalmic educational materials. For this reason, we created an open access website through the Moran Eye Center at the University of Utah with a dedicated outline for medical students (http://morancore.utah.edu). This outline is based on an Association of University Professors of Ophthalmology (AUPO) Medical Education Task Force editorial that identified the core ophthalmologic knowledge and skills expected of all United States medical school graduates (4). As ophthalmologists, we have a responsibility to take ownership over the education of our nonophthalmologic colleagues. They serve on the frontline screening for eye disease and making appropriate and timely referrals. So often the educational materials produced by ophthalmologists are targeted toward only those who have completed ophthalmic training, and the materials can be difficult to understand for nonophthalmologists. The articles in the medical student outline in the Moran CORE (clinical ophthalmology resource for education) take a novel approach. First, they have gone through a peer review and are posted through a reputable institution. Second, these articles have been written by medical students who have a unique insight into identifying what is of greatest benefit to their classmates while making it conceptually accessible. Although these articles are edited and reviewed by staff, we have been impressed how medical students explain topics in clear terms with understandable concepts. We know that both clinicians and patients will increasingly use the internet for self-directed learning, diagnosis, and management of eye diseases. It is hoped that the resource we are providing will give users the confidence and peace of mind that they are accessing accurate and reliable information. We welcome any feedback.
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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 | 0.004 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".