The Firefly digital otoscope as an aid to teaching otoscopy in primary care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Teaching otoscopy within primary care can be a challenge, as there is a need to teach both the psychomotor skills required to perform otoscopy, as well as the interpretation of the clinical findings.[1] Basic otoscopy skills are often lacking and the clinical slides that are used in classroom teaching to show both the normal anatomy of the ear and the commonly encountered pathological findings are usually taken with special medical and photographic equipment, so they are often unrepresentative of what primary care otoscopists will encounter in their clinical practice. This can make it difficult for the novice otoscopist to translate what they are seeing through an otoscope to the images that they have seen in a textbook, or from an on-line library source. In a Canadian study, it was found that 95% of medical graduates were not comfortable with their otoscopy skills and that on testing general practitioners (GPs) and paediatricians in their ability to make an accurate otoscopic diagnosis there was less than 50% accuracy.[2] This article describes how a digital otoscope can be used as a teaching aid in primary care to train medical students, doctors in training and practice nurses to develop or improve their otoscopy skills and clinical knowledge.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it