An endoscopic Ear Trainer for the low-resource setting
Bibliographic record
Abstract
BACKGROUND: Endoscopic ear surgery is a technique that is growing in popularity. It has potential advantages in the low-resource setting for teaching and training, for the relative ease of transporting and storing the surgical equipment and for telemedicine roles. There may also be advantages to the patient, with reduced post-operative pain, facilitating the ability to complete procedures as out-patients. METHODS: Our Ear Trainer has previously been validated for headlight and microscope otology skills, including foreign body removal and ventilation tube insertion, in both the high- and low-resource setting. This study aimed to assess the Ear Trainer for similar training and assessment of endoscopic ear surgery skills in the low-resource setting. The study was conducted in Uganda on ENT trainees. RESULTS: Despite a lack of prior experience with endoscopes, with limited practice time most participants showed improvements in: efficiency of instrument movement, steadiness of the camera view obtained, overall global rating of the task and performance time (faster task performance). CONCLUSION: These results indicate that the Ear Trainer is a useful tool in the training and assessment of endoscopic ear surgery skills.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".