Bibliographic record
Abstract
BACKGROUND: Fundoscopy is viewed as a difficult or impossible task by many students and physicians. We have used a novel seven-step approach to teach trainees to use the ophthalmoscope. The technique is based on the premise that success is most easily achieved if the necessary motor skills are mastered first. A step by step approach will enable others to teach their trainees to attain the ability to routinely view the fundi of their pediatric patients. METHODS: Step 1 involves examination of the trainee's fundi to ensure there is no impediment to their success. In Step 2 the student examines the teacher. This identifies major errors. The next step teaches the trainee how to hold the ophthalmoscope. Step 4 gets the learner to read a journal article through the ophthalmoscope. In Step 5 they examine the teacher's eyes again and with a little help they are always successful. In the last two steps an older patient is first examined and finally the student examines a young child. CONCLUSION: This method differs from most other approaches by leaving the cognitive component of ophthalmoscopy until the student is comfortable with handling the instrument. It has been uniformly successful among our students and residents.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".